Automatic energy management and energy consumption reduction, especially in commercial and multi-building systems

ABSTRACT

Automatic energy management is provided, in even the most complex multi-building system. The necessity of a human operator for managing energy in a complex, multi-building system is reduced and even eliminated. Computer-based monitoring and computer-based recognition of adverse energy events (such as the approach of a new energy peak) is highly advantageous in energy management. Immediate automatic querying of energy users within a system of buildings for energy curtailment possibilities is provided. Such immediate, automatic querying may be answered by the energy users through artificial intelligence and/or neural network technology provided to or programmed into the energy users, and the queried energy users may respond in real-time. Those real-time computerized responses with energy curtailment possibilities may be received automatically by a data processing facility, and processed in real-time. Advantageously, the responses from queried energy users with energy curtailment possibilities may be automatically processed into a round-robin curtailment rotation which may be implemented by a computer-based control system. Thus, impact on occupants is minimized, and energy use and energy cost may be beneficially reduced in an intelligent, real-time manner. The invention also provides for early-recognition of impending adverse energy events, optimal response to a particular energy situation, real-time analysis of energy-related data, etc.

BACKGROUND OF THE INVENTION

[0001] This invention relates generally to systems and methods formanaging use of energy, and especially to systems and methods formanaging energy use in a complex multi-building context.

[0002] A number of factors have combined in recent years to create anelectrical energy crisis in many regions of the United States. Theseinclude: a shortage of generating capacity; lack of capital investmentin new transmission capacity; fuel volatility; and increased demand. Theresult is a power shortage and difficulties in the energyinfrastructure.

[0003] Multiple-building systems, such as commonly owned systems of 30,60 or more buildings, exist throughout the world today. Examples of suchbuilding systems include, e.g., university systems. Multiple buildingsystems may be geographically dispersed. Controlling energy consumption,and costs of energy consumption, in such wide-spread building systemspresents challenges. See, e.g., U.S. Pat. No. 6,178,362 issued in 2001to Woolard et al. (assigned to Silicon Energy Corp.), discussing some ofthe problems of energy management and energy cost management forcommercial users who operate large physical plants.

[0004] Conventionally, if it was desired to reduce energy consumption bya particular amount (such as a 40 KW reduction in the next two hours) ina multi-building system, which typically use procedure-based systems(such as conventional building management systems, current-generationenergy management software, or SCADA-type systems), the building managerwas required to conduct all the steps and tasks necessary to accomplishthe goal manually. Thus, the question of how to accomplish a specifiedenergy consumption reduction has been heavily human-dependent.

[0005] Another question is how to know what specific energy consumptionreduction to even want to accomplish. That question, too, has beenheavily human-dependent. For example, conventionally, as in U.S. Pat.No. 6,178,362, various meters and data-taking devices have been includedin multi-building systems, but the obtained energy data still must bereviewed by a human operator. The necessary inclusion of a humanoperator in conventional systems has posed certain substantialdisadvantages. A human operator may fail to recognize one or moreenergy-relevant events (such as the threat of a new maximum peak). Thediligence, accuracy, speed, and foresight of a human operatornecessarily may be limited, contributing to likely missed recognition ofsuch energy relevant events. Human operators may have other duties, sothat they not be reviewing relevant energy data at what would be acritical time. Human operators may review data yet fail to appreciateits significance. Human operators may review data, appreciate itssignificance, and decide on a course of action that may be less thanoptimal in terms of cost or convenience or comfort.

[0006] In any energy management system, reaching a new maximum of peakusage will be expensive and is acknowledged as something to berecognized—and avoided. In a human-based energy management system, thehuman operator may, or may not, be looking at energy data output at atime when the data is surging towards a new peak. Human operators comein a variety of diligence, attentiveness, and ability levels. Humanoperators tasked with recognizing surges towards new peaks tend to haveother tasks, such that they cannot provide a sufficient level ofattention and monitoring to recognize every surge towards a new peak.

[0007] Recognition of an energy-relevant event such as a surge towards anew peak is only one aspect of energy management. After recognition thatan undesirable energy-relevant event is in progress, there remains thequestion of what response to take. There is only so much information andso many permutations that a human operator possibly can take intoaccount in a fixed amount of time. The human operator is called upon todecide and act quickly, to avoid the new peak toward which the system issurging. When a human operator recognizes that a new energy peak isbeing approached, he or she will want to act quickly to avoid reachingthe peak and will make a decision to reduce power to one or more powerconsumers in the system. The human operator is essentially incapable ina limited amount of time of consulting or studying the many differentenergy users (such as energy-using devices or apparatuses such asair-conditioners, etc.) to ascertain the status of each. A humanoperator practically speaking can do no more than, at best, execute oneor more energy-reducing commands-for at least the reason that the luxuryof time is not present.

[0008] Software systems that reduce energy consumption in building havebeen available for many years. These systems work by connecting variouspieces of energy-consuming equipment to a computer, which allows thebuilding manager to monitor consumption, and, if necessary, manuallyreduce it. More sophisticated systems allow third party “servicebureaus” to provide these functions for building owners, but they stillrely on intensive human intervention to be effective. Heretofore, theanalysis and management of energy consumption has been a manual process.Computers and software systems have been able to collect data on energyconsumption in particular facilities or on individual pieces ofequipment for years. But human beings have had to analyze thatinformation, and decide what action to take to reduce energyconsumption. And because many factors affect energy consumption at anygiven moment—the weather outside, the number of people inside, etc.—ithas never been possible to accurately and precisely adjust energyconsumption in real time. For example, the Woolard et al. system seeksto use three dimensional facilities navigation tools, energy consumptionanalysis processes, TCP/IP communication and a World Wide Web(WWW)-based interface, but it is based on sub-systems each of which“performs operations which permit an employee of the entity to controland manage its facilities including its energy consumption.” Id., column2, lines 26-29 (emphasis added).

[0009] The electricity crisis in California in 2001 provides a vividillustration. Although many buildings and factories in the state haveenergy management systems, the only option available to power suppliersand commercial consumers trying to prevent wholesale network collapsewas literally to turn out the lights in “brownouts” and rollingblackouts. The energy management systems in place and the people whomonitor them on a daily basis were simply not capable of analyzing allof the potential alternative for reducing energy consumption and doingso quickly. The only choice was to shut down whole systems andbusinesses. Power outages, even planned power outages, have highlydisruptive effects, such as disrupting telephone and computer networkequipment, data inaccessibility, etc.

[0010] The various government and quasi-government entities charged withensuring energy availability will continue to push users to curtailtheir electric power usage in order to avoid the devastating impact ofblackouts, either actual or threatened. Avoidance of power outages bylarge users of power is sought, as having many benefits. Businesses needto have reliable sources of energy. Governments face social andpolitical consequences of chronic energy shortages. Power supplierscannot meet the demand for electricity in their areas, without buildinglarge power-generating reserves, which is not an optimal solution. Thus,it will be appreciated that there are many challenges in the areas ofenergy consumption, energy shortages, and energy management that remainto be addressed.

SUMMARY OF THE INVENTION

[0011] In the present invention, a system comprising artificialintelligence is connected to energy-using devices (such as pieces ofequipment). Energy consumption advantageously may be monitored and/ormanipulated in real time. Artificial intelligence (such as intelligentagents) may be used to evaluate, forecast and/or control energyconsumption patterns. From the system comprising artificialintelligence, control signals may be sent to deploy agreed-uponenergy-saving strategies at the building and/or device (energy user)level. Advantageously, energy management can be autonomous,artificial-intelligence based, real-time, over the Internet.

[0012] A significant advantage of the invention is to provide maximumenergy curtailment with minimal impact to occupants of buildings in thebuilding system. Maximum energy curtailment may be achieved with nogreater than a certain defined level of impact to occupants of buildingsin the building system.

[0013] The invention in a first preferred embodiment provides an energymanagement system comprising: computer-based monitoring for an adverseenergy event in a building system; computer-based recognition of anadverse energy event in the building system; immediate automaticquerying of energy users within the building system for energycurtailment possibilities; automatic receipt of responses from queriedenergy users with energy curtailment possibilities; automatic processingof energy curtailment possibilities into a round-robin curtailmentrotation. Preferably, responses from queried energy users with energycurtailment possibilities are automatically processed by a computer witha set of instructions for evaluating how to enact each respectivecurtailment possibility of each respective energy user offering acurtailment possibility.

[0014] In another preferred embodiment, the invention provides a methodfor minimizing and/or eliminating need for human operator attention inenergy management of a building system, comprising: non-human,computerized processing of obtained energy data, wherein the obtainedenergy data is for at least one energy user in the building system, saidprocessing including (A) automatic determination of whether at least oneenergy-relevant event is present or (B) continual optimization of asetting of the at least one energy user. Optionally, when aenergy-relevant event is automatically determined to be present, theinvention provides immediately activating an automatic response to theenergy-relevant event. Another preferred but optional example ismentioned, wherein at least one intelligent agent, from the obtainedenergy data, actually forecasts the peak; wherein the energy-relevantevent is a threat of a new maximum peak, and the immediately activatedautomatic response includes energy reduction interventions to avoid thenew maximum peak.

[0015] In a further preferred embodiment, the invention provides acomputer-based energy management system, comprising: non-human,computerized processing of obtained energy data, wherein the obtainedenergy data is for at least one energy user in a building system, saidprocessing including automatic determination of whether at least oneenergy-relevant event is present; and upon recognition of an automaticdetermination that at least one energy-relevant event, a non-human,computerized response thereto based upon artificial intelligencereasoning.

[0016] Additionally, in another preferred embodiment the inventionprovides a computer-based round-robin rotation system for energy users,wherein the energy users are under computer-based control and arepresent in a building system, the round-robin rotation systemcomprising: a series of computer-based energy curtailment commands toeach of a plurality of energy users in the building system, wherein (1)each computer-based energy curtailment command in the series of energycurtailment commands; (a) is specific to the energy user to which thecurtailment command is directed; (b) has been derived from an energycurtailment offer provided by the energy user; and/or (c) is based oncontinually learned and observed characteristics of the energy user;and/or (2) an energy user in the plurality of energy users is groupedwith other energy users based on similarity with regard to a certainparameter or parameters.

[0017] The invention, in another preferred embodiment, provides acomputer based method of avoiding a new energy peak, comprising: priminga computer-based system with data as to energy peak(s) already reachedin a building system; for current energy usage in the building system,obtaining, in real-time, computer-readable data from which toautomatically forecast if a new energy peak is approaching; andreal-time automatic processing the obtained computer-readable data toforecast whether or not a new energy peak is approaching. Preferably, ifthe real-time automatic processing of the obtained computer-readabledata provides a forecast that a new energy peak is approaching, animmediate, real-time, automatic response is initiated.

[0018] In a further preferred embodiment, the invention provides anenergy curtailment system comprising an automatically managedround-robin rotation of a plurality of energy curtailment interventions.Each respective energy curtailment intervention within the plurality ofenergy curtailment interventions may derived from an energy curtailmentoffer from a to-be-curtailed energy user. A plurality of to-be-curtailedenergy users may be included in a single building or in a multi-buildingsystem.

[0019] Additionally, the invention in yet another embodiment provides acompilation of energy-relevant data, comprising: a stream ofenergy-related data for at least one individual energy user within aplurality of energy users (such as where the at least one individualenergy user is within a multi-building system and separate streams ofdata are provided for other individual energy users within themulti-building system.)

[0020] The invention also provides a data analysis method, comprisingleveraging a stream of energy-related data for at least one individualenergy user within a plurality of energy users, wherein the leveragingincludes a comparison against historic data for the device. Theleveraging may include computer-based searching for rapid deviation froma historic pattern.

[0021] Another embodiment of the invention provides a method ofdetermining whether to repair or replace an individual energy user,comprising: reviewing a stream of energy-related data for the individualenergy user, wherein the individual energy user is contained within aplurality of energy users.

[0022] The invention in an additional embodiment provides an energymanagement system for automatically achieving energy curtailment in amulti-building system, comprising: immediate automatic querying ofenergy users within the building system for energy curtailmentpossibilities; automatic receipt of responses from queried energy userswith energy curtailment possibilities; automatic processing of energycurtailment possibilities into a round-robin curtailment rotation.

[0023] Some perfecting details of the inventive systems, methods, etc.are mentioned as follows, without the invention being limited thereto.

[0024] Preferably, each energy user has associated therewith a dedicatedneural network, such as a dedicated neural network that continuouslylearns operating characteristics of said energy user associated with thededicated neural network, wherein forward and backward reasoning andforecastability are provided.

[0025] Where an adverse energy event or energy-relevant event ismentioned, examples may be a new peak demand or threat thereof; ahuman-given directive to curtail a certain amount of energy consumption;and/or an excess increase of energy price in a deregulated market. Theadverse or energy-relevant energy event may be a surge or a steadyincrease towards a new peak demand; at least one recognizable pattern ofdata that has been learned via artificial intelligence by a computersystem doing the monitoring; etc. Preferably, the computer doing therecognition of an adverse energy event, for each recognized pattern ofdata that is an adverse energy event, reacts with an automatic responsebased upon reasoning (such as a a querying response to be executed).

[0026] Where monitoring is mentioned, the monitoring may occur in acontext selected from a business-as-usual context; 24×7 permanent loadreduction context; and an emergency context. Energy use may beconstantly monitored and/or adjusted, said constant monitoring and/oradjustment being non-human, wherein business-as-usual constantadjustment, 24×7 load reduction is provided. The non-human constantmonitoring and/or adjustment preferably is by artificial intelligence;and, preferably is to monitor and/or adjust at least one factor thatinfluences energy consumption (such as current weather conditions atand/or approaching an energy-user; occupancy levels of a facility servedby an energy-user; market price of energy; weather forecasts; marketprice forecasts; air quality; air quality forecasts; lighting quality;lighting quality forecasts; plug load patterns; plug load patternforecasts; etc.).

[0027] The invention may include and/or provide one or more of thefollowing:

[0028] at least one modeling agent and/or at least one forecastingagent;

[0029] 24×7 permanent load reduction;

[0030] minimization of energy consumption in ongoing business-as-usualenergy consumption;

[0031] load balancing between buildings;

[0032] automatic documentation of energy savings attributable to anyautomatic intervention(s) by the energy management system;

[0033] machine-based learning from the obtained data and/ormachined-based constructing a model from the obtained data;

[0034] automatic documentation of energy savings attributable to anysaid automatic intervention(s);

[0035] machine-based reasoning to select between at least twoconflicting goals (such as machine-based reasoning is to select betweena market price goal and a comfort-maintenance goal);

[0036] a computerized display of energy data and/or device;

[0037] on human demand, computerized forecasting, computerizedsimulation of an effect or effects of a proposed control action, and/orcomputerized reporting on simulation at various levels of aggregation;

[0038] artificial intelligence reasoning based on one or more of: (A)knowledge about a building or buildings in the building system, (B)knowledge about an energy using device, (C) knowledge about the buildingsystem, and (D) data outside the building system;

[0039] automatic querying of energy users;

[0040] receiving responses from queried energy users and automaticallyprocessing the received responses;

[0041] automatic formulation of an optimal energy-saving commanddecision and/or strategy;

[0042] executing the optimal energy-saving command decision or strategy;

[0043] generation of a log of historical activity by one or moreartificial intelligent agents performing the artificial intelligencereasoning;

[0044] machine-based detection of presence of a chemical or biologicalwarfare agent, to which is determined a machine-based response (such asrelease of an anti-agent and/or adjustment of one or more energy users);at least one machine-based determination of at least one parameter ofinterest to a building manager, said parameter being measurable andcontrollable;

[0045] automatic monitoring of the computerized response;

[0046] communication over the Internet;

[0047] learning by artificial intelligence that a desired targetparameter (such as room temperature) in each area served by the systemcan be maintained by a round-robin rotation;

[0048] compiling a complete array of historical data incomputer-readable form, determining one or more patterns thereform, andcomparing therewith current real-time data to forecast if a new peak isgoing to be reached;

[0049] neural network based prediction;

[0050] one revenue-grade virtual meter which is an aggregation ofrevenue-grade meters;

[0051] monitoring and adjusting based on all of current weatherconditions at and/or approaching an energy-user; occupancy levels of afacility served by an energy-user; market price of energy; weatherforecasts; market price forecasts; air quality; air quality forecasts;lighting quality; lighting quality forecasts; plug load patterns; andplug load pattern forecasts;

[0052] preliminary functional testing for obtaining data and formulatingapplicable rules, and a continuous process of learning embedded in aneural net of a modeling agent associated with an energy-using device.

[0053] Where a building system is mentioned, the building system may bea single building or at least two buildings. The building or buildingsmay be, for example, at least one university building; at least onehotel building; at least one hospital building; at least one cardealership building; at least one shopping mall; at lease one governmentbuilding; at least one chemical processing plant; at least onemanufacturing facility; and any combination thereof of buildings.

[0054] When at least two buildings are provided, the at least twobuildings under management may be geographically dispersed (such as astate's difference apart); and/or commonly owned or not commonly owned.Ownership may be, for example, by a commercial entity, a university, agovernment, etc.

[0055] Where a peak is mentioned, examples of a peak include a kW demandpeak, a lighting peak, a carbon dioxide peak, a pollutant peak, etc.

[0056] Where an automatic response is mentioned, preferably theautomatic response is non-determinative.

[0057] There has been mentioned automatic determination of whether atleast one energy-relevant event is present, and preferably suchautomatic determination comprises application of artificialintelligence.

[0058] When artificial intelligence is mentioned, preferably theartificial intelligence is that of neural networks; rule-based expertsystems; and/or goal-based planning systems. The artificial intelligencereasoning may comprise at least one artificial intelligent agent and,optionally, at any given time, what the artificial intelligence agent isdoing may be monitored (such as monitoring by a human viewing what theartificial intelligence agent is doing.).

[0059] One or more of the following may be provided and/or included:more obtained energy data is processed in a given time period than couldbe processed by a human being; a non-human, computerized response may beformulated after processing of more information than could beaccomplished by a human in whatever processing time has been expended;monthly energy consumption may be reduced for the building system and/orpeak load demand charges for the building system are lowered. Wherecomputerized reporting has been mentioned, the aggregation level for thecomputerized reporting may be at an individual device, at everything ina building, at a set of buildings, or everything commonly owned. Theoptimal energy-saving command decision may comprise a rotation of energycurtailment that minimizes impact over energy users in the system.

[0060] Where a computerized response has been mentioned, thecomputerized response may include at least one determination based onone or more of: (A) air quality, humidity, pollutants, air flow speed,temperature, and other descriptors of physical properties of air; (B)light direction, light color, ambient temperature, foot candle, kwconsumption of light producing equipment, smell of light, and otherdescriptors of physical properties of light; (C) plug load; motionsensed by motion sensors; carbon dioxide levels; brightness; soundlevels; automated device for sensing human presence; motion detectors;light-sensing apparatus; habitation-sensor; (D) chemical or biologicalwarfare agent sensing device (such as a mustard gas sensor, an anthraxsensor, a carbon monoxide sensor, a carbon dioxide sensor, a chlorinegas sensor, a nerve gas sensor, etc.).

[0061] Where a round-robin rotation system has been mentioned, examplesmay be a round-robin system formulated in response to a human requestfor energy curtailment; a round-robin system implemented underbusiness-as-usual circumstances, etc. By way of example, a computer mayautomatically process the responses from queried users, total therespective curtailment possibilities from the queried energy usersamounts, determine whether the total of respective curtailmentpossibilities is sufficiently large, and, (A) if so, proceed to schedulea round-robin energy curtailment rotation pursuant to criteria; and, (B)if not, notify a human user. The round-robin curtailment rotation may beexecuted and achieve energy consumption reduction. The energyconsumption reduction may occur during an energy emergency (such as,e.g., an energy emergency declared by a local independent systemoperator, a power authority, a utility supplier, or a governmentalauthority, etc.). A round-robin curtailment rotation may be called inorder that energy may be sold back into the grid.

[0062] Where computer-readable data has been mentioned, thecomputer-readable data may comprise data from the energy users in thebuilding system; from a source selected from sensing devices, electricmeters used for billing, and information from individual devices; etc.

[0063] Demand for each individual device may be forecast based ontemperature forecasts; patterns historically observed and learned viaartificial intelligence and under continual update; and occupancy wherethe individual device is provided.

[0064] Where immediate automatic querying has been mentioned, suchquerying may be directly or indirectly activated such as querying basedon, for example, a request by a local independent system operator, apower authority or a utility supplier.

[0065] The invention provides optional documentation, such automaticdocumentation automatically generated of avoidance of a new energy peak,with said automatic documentation being (a) stored in an accessiblecomputer file and/or (b) printed and/or stored in a humanoperator-friendly format.

[0066] The invention advantageously makes possible that, if desired, ahuman operator is not needed. If desired, a human operator may have anoptional override right. Also optionally, a human operator may enter aquery (such as a query as to current state of one or more devices in aspecified building, a query requesting a prediction of effect ofproposed control action(s) on an energy bill and/or on comfort, etc.).The invention includes an embodiment wherein no human operatorintervention is involved in either the automatic processing to forecastwhether or not a new energy peak is approaching nor the immediate,real-time, automatic response to the forecast that a new energy peak isapproaching. By applying the invention, new energy peaks may be avoidedwithout human operator intervention. Advantageous results (such asenergy consumption reduction) mentioned herein may be achieved even whenno human is controlling.

BRIEF DESCRIPTION OF THE DRAWINGS

[0067]FIG. 1 is a flow chart of an exemplary inventive energy managementsystem which is machine-based and may operate human-free.

[0068]FIG. 2 is a flow chart of exemplary machine-based energy datareceipt and processing, including automatically identifying andresponding to an adverse energy event, according to the invention.

[0069]FIG. 3 is a flow chart of an exemplary machine-based energycurtailment response to an adverse energy event, according to theinvention.

[0070]FIGS. 4A and 4B are examples of schematic diagrams of therelationship of user-set goals to be effectuated by higher level agentsand the higher level agents, according to the invention.

[0071]FIG. 5 is a diagram of an exemplary Internet-based energymanagement system of three buildings, according to the invention.

[0072]FIG. 6 is flow chart for an exemplary round robin algorithm forload rotation, according to the invention.

[0073]FIG. 7 is a chart of an exemplary rotation schedule/matrix exampleaccording to the invention, with a load rotation “round robin” approachbeing shown.

[0074]FIG. 8 is a graph of Peak Load: exemplary Virtual Meter accordingto the invention versus Real Meters.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0075] As may be seen with reference to FIG. 1, in one preferredembodiment, the invention is a machine-based energy management system,which may be human-free in operation. Although a human operator is notneeded, a human operator is not necessarily precluded from acting in theenergy management system. In the energy management system of FIG. 1, fordata received electronically from a plurality of energy users, anadverse energy event is monitored-for electronically (100). If theelectronic monitoring 100 detects no adverse energy event, theelectronic monitoring for an adverse energy event continues (100A) asmore to-be-monitored data is electronically received. If the electronicmonitoring 100 electronically detects an adverse energy event 110, theadverse energy event that has been electronically detected 110 iselectronically acted-upon 120 by adjustment of at least some of theplurality of energy users. (Herein, reference is made, variously, toelectronically-done steps, machine-based activity, computer functioning,electronic activity, automatic activity, and the like, in each case, tomake clear that the step is performed in a non-human manner rather thanto limit reference to a particular machine or device.)

[0076] The monitored-for adverse energy event may be any energy-usageand/or energy-cost related event, for which the received to-be-monitoreddata may be electronically monitored. As a preferred example of anadverse energy event for which the received energy data may be monitoredis mentioned the surge towards a new energy peak. It will be recognizedand appreciated that a machine, such as computer, may more rapidlycalculate and compare numerical information than could a human operator.Presented with the same electronic energy data, a machine-based systemcan more rapidly and accurately arrive at a faster conclusion as to thedirection being taken by the energy use in the entire system. Thegreater the number of energy users, the greater the number of buildingsin the system, and the greater the dispersion of energy users overdifferent buildings, the more difficult it is for a human operator orfor several human operators to make decisions that minimize energy usageand cost of energy usage without adversely affecting operations andoccupants.

[0077] The invention may be used in any system including a plurality ofenergy users, most preferably a system in which the plurality of energyusers are dispersed in multiple commercial buildings. The inventionprovides particular advantage in a multi-commercial building system,because of the difficulties otherwise posed by energy management andenergy cost control in such multi-commercial building systems. Asexamples of commercial buildings may be mentioned, e.g., universitybuildings, factories, hospitals, office buildings, etc. It will beappreciated that not necessarily all energy users in a building arerequired to participate in the energy management system of theinvention.

[0078] As examples of an “energy user” in the present invention may bementioned any device that requires energy to operate, including but notlimited to, e.g., air conditioners, chillers, heating and ventilation,fans, fountain pumps, elevators, other equipment, lighting, etc.

[0079] As examples of the to-be-monitored energy data receivedelectronically from the energy users are any data that are receivablefrom an energy user in real-time (i.e., embedded) communication with adata-receiving device. As examples of such data movement may bementioned an electronic means of real-time data movement such as anetwork (such as the Internet (i.e., the World Wide Web (WWW), anintranet, etc.), most preferably, the Internet. As examples of thesources of the machine-readable data that is received and subjected tomachine-based monitoring may be mentioned any metering device, measuringdevice, etc. that measures energy use (including actual use andscheduled upcoming use) of an energy user.

[0080] With reference to FIG. 1, the electronic-acting upon 120 adetected adverse energy event may be any response or adjustment thatreduces energy cost and/or energy usage, most preferably an energy costreduction and energy usage reduction approach with minimal impact onoccupant comfort and normal operations. An exemplary electronic response120 to a detected adverse energy event may be seen with respect to FIG.2, in which the energy management system provides electronic receipt ofdata from a plurality of energy users (200). The received data iselectronically processed (210) vis-à-vis whether an adverse energy event(such as approach of a new peak) may exist, and when an adverse energyevent is detected, the system electronically requests energy curtailmentpossibilities (220) from some or all of the plurality of energy users.Energy curtailment possibilities from energy users are electronicallyreceived (230), and received energy curtailment possibilities areautomatically processed (240).

[0081] In the electronic request for energy curtailment possibilities(220), it is not required that all energy users be queried for energycurtailment possibilities. For example, certain energy users that aredeemed essential may be excluded from being part of an automatic queryfor energy curtailment possibilities. The requests for energycurtailment possibilities are directed to such energy users that havethe ability to consider their energy curtailment possibilities and toformulate an energy curtailment response (such as an offer of kilowatthours to forego). Thus, the energy users to be queried are supplied withsuch artificial intelligence, neural network technology, or othercomputer- or machine-based technology and programming such that they cancompute, in real-time, what energy curtailment they can offer based oncertain preset rules applicable to the respective energy user, such asrules relating to current weather conditions, comfort, etc. Thus, itwill be appreciated that respective energy users will have programmingsuitable for the context in which the energy user operates. For example,an air conditioning energy user and a multi-elevator energy user will beprogrammed to consider different factors for evaluating whether each canuse less energy. An air conditioning energy user may be programmed toconsider outside temperature and time of day and other factors, while amulti-elevator energy user may consider time of day and not beprogrammed to consider outside temperature. The multi-elevator energyuser may place much different emphasis on time-of-day than the airconditioning energy user. For example, because shutting down elevatorsat certain high-traffic times of day may achieve an energy savings butbe unacceptable from a building management viewpoint, the elevatorenergy user's formulation of an energy curtailment possibility responsemay heavily depend on the time of day.

[0082] Each to-be-queried energy user thus is provided with a means tointelligently respond in real-time with an appropriate response that isminimally-invasive or bothersome to the building occupants and thosebeing served by the queried energy user.

[0083] With reference to FIG. 2, it is particularly mentioned that theenergy curtailment possibilities from the energy users areelectronically received (230) and automatically processed (240). Thatis, reliance on a human operator advantageously is not needed.

[0084] It will be appreciated that, in a preferred embodiment, theinvention provides for the energy management system of FIG. 3, in whichthe system provides real-time machine-based evaluation of data forenergy curtailment possibilities (340), from which an automaticround-robin energy curtailment rotation is established (350). Eachaffected energy user is automatically advised of the energy curtailmentthat the affected energy user is to implement (360) as its part in theround-robin energy curtailment rotation. It will be appreciated that ahuman operator or operators (especially in a multi-building system)cannot formulate an optimal round-robin energy curtailment rotation inthe short time that a machine-based system can. Moreover, even once amachine-based energy curtailment round-robin rotation is established, itwill be appreciated that it is much preferred for a machine-based systemto electronically implement the established rotation, compared to humaninvolvement in controlling the to-be-curtailed energy users. While humaninvolvement in the inventive energy management system is not prohibited(such as the ability of a human operator to override or command that acertain feature of the established rotation not be implemented),preferably human operator involvement is limited or none.

[0085] With reference to FIGS. 4A and 4B, the relationship of triggeragent(s) 1 (such as user-set goals or peak load) for higher level agentsto accomplish, higher level agents that are to accomplish those user-setgoals, and devices 2, 2A, 2B may be seen in two exemplary uses of theinvention. As examples of trigger agent 1 when the trigger agent 1comprises user-set goals for higher level agents to accomplish may bementioned: impact on occupants expressed in computer-based terms; pricesensitivity expressed in computer-based terms, etc.

[0086] Energy-using devices 2, 2A, 2B are shown, but more sets ofdevices may be incorporated into the system. In the case of the threesets of devices 2, 2A, 2B, three are shown for manageability ofillustration, and not to indicate any limitation of the invention inthat regard. The same comment applies to other features shown herein,such as buildings 10, 10A, 10B.

[0087] Device 2 is provided with a forecasting agent 3, a modeling agent4 and a control agent 5. Respectively, device 2A is provided with aforecasting agent 3A, a modeling agent 4A and a control agent 5A anddevice 2B is provided with a forecasting agent 3B, a modeling agent 4Band a control agent 5B. Preferably, each device (energy user) has adedicated neural network that continuously learns the operatingcharacteristics of that energy user, and allows forward and backwardreasoning, thereby making forecasts. For example, the neural network maylearn that if the temperature setting of an air conditioning unit isbumped by 2 degrees, that a certain drop in kW consumption results. Theneural network may learn that if kW consumption is dropped by 3 kW, acertain temperature effect is observed. The neural network may learnthat if a certain event or device setting adjustment occurs, the timeelapsed before an OSHA level is reached is a certain amount. These areonly a few examples of reasoning by the neural network.

[0088] As a modeling agent 4 may be used any system configurable as aneural network that may be disposed with respect to an energy user(device) to learn (preferably, to continuously learn). Examples of whatmay be learned about an associated device by a modeling agent include,e.g., energy consumption (kW), temperature, degradation time, fan speed,vane position, etc. A modeling agent preferably continually learns theoperating characteristics of the device with which it is associated,thus understanding, for example, the connections between energyconsumption (kW) and room temperature for an air conditioner. Aforecasting agent predicts energy consumption of the device associatedtherewith under various conditions, allowing simulation and curtailmentdecision making. A device control agent takes control of the device.

[0089] As a forecasting agent 3 may be used any system that, uponreceiving a question, returns to the modeling agent 4 and runs thequestion. Preferably, the forecasting agent 3 neither over-norunder-generalizes. For reducing over- and under-generalization by theforecasting agent 4, it is preferred for the modeling agent 4 to havebeen in continual learning for as long a time period as possible, withrelatively longer times of continual learning being more preferable. Forexample, a modeling agent that has been in continual learning for aone-day period has only a certain limited number of data points and if aquery is posed to the forecasting agent and the forecasting agent cannotfind an exact match of data points in the modeling agent, theforecasting agent will need to generalize (i.e., extrapolate) and isrelatively likely to over- or under-generalize. If the modeling agenthas been in continual learning for a five-year period, if the same queryis posed to the forecasting agent, the forecasting agent is relativelymore likely to find a match, or at least a closer match, of data pointsin the modeling agent, and thus the forecasting agent is relatively lesslikely to over- or under-generalize.

[0090] Higher-level control agents run on the portfolio or buildinglevel, controlling many devices (via their control agents). Based onuser-set goals and environmental input (such as price of energy,temperature, occupancy, etc.), the higher level agents devise a strategyto achieve the user-set goals and accomplish the user-set goals bycontrolling the device agents. Higher-level agents reason via artificialintelligence to find a suitable balance between two or more goals, suchas between savings and comfort.

[0091] A higher level control agent (for load rotation) 6 is provided inFIGS. 4A and 4B. In FIG. 4B, a second higher level control agent (forload rotation) 6′ also is provided, showing a situation where loadrotation may have two different portfolios. An example of a contextrepresented by FIG. 4B may be where different strategies are in place.Examples of a strategy include, e.g., a permanent 24×7 load rotation; acurtailment load rotation. A strategy or strategies is or are embodiedby one or more artificial intelligent agents.

[0092] An artificial intelligent agent may be concerned with a strategysuch as price sensitivity; air supply; temperature, etc. Intelligentagents can be used to reduce energy cost better than automated buildingmanagement systems and/or human experts. Herein, examples are givenshowing how intelligent agents using continuous learning and reasoningcan manage energy better than conventional building management systems.Both, the cost of energy for large buildings and the comfort for tenantsare taken into account. The scenarios of the Examples herein highlightmajor differences between knowledge based energy management andconventional, schedule-driven energy management. From the Examples andComparative Examples herein, it can be seen that continuous learning ismore accurate than one-time settings; accurate forecasting enables smartplanning; flexible responses to curtailment requests are provided; awider range of information inputs means better building intelligence;rigid scheduling cannot accomplish the results of knowledge-basedreasoning; feedback loops are too simplistic for today's sophisticatedenergy management concepts; trial and error methods are too costly

[0093] Referring to FIGS. 4A and 4B, a higher level control agent 7 thatis price sensitive is provided. Control agents 6, 6′ (in FIG. 4B) and 7are intelligent agents.

[0094] For connecting to buildings in which the devices 2, 2A, 2B arelocated, suitable hardware and software (such as platform hardware andsoftware provided by Engage Networks and Silicon Energy) are used. Abuilding equipped with a building management system (BMS) provides theeasiest connection to the system comprising the intelligent agents(i.e., the higher-level control agents), as well as to individualenergy-consuming devices 2, 2A, 2B. When BMS is used, as a platform maybe mentioned any communications protocol that allows quick and seamlesscommunication with the BMS and the devices it monitors. As a mostpreferred example may be mentioned BMS platforms with opencommunications protocols such as BacNet via UDP, which allow quick andseamless communication between the system comprising the intelligentagents with the BMS and the devices it monitors. If the BMS is not open(i.e., does not adhere to an open communications standard, such asBacNet or OPC), appropriate drivers may be obtained and used tocommunicate. For example, the BMS manufacturer may be contacted to buyor otherwise obtain the driver or at least the specifications for thedriver to talk through the Internet to the BMS through them. Examples ofsuch BMS drivers include, e.g., control drivers by Johnson, Invensys,Honeywell, etc. As software useable in the invention may be any softwarethat allows communication with a BMS such that remote control can beachieved.

[0095] Advantageously, the present invention is installable inconjunction with certain existing equipment and software. For example,hardware devices may be installed that can translate between protocolsand conduct simple data buffer or transfer tasks. Existing monitoringsystems (such as those provided by Engage Networks and Silicon Energy)may be leveraged, for connecting portfolios of buildings. Such existingmonitoring systems which allow end-users to manually control a BMS arelacking in any artificial intelligence capability, and that artificialintelligence capability is thus supplied by the present invention. Thepresent invention, by operating in connection with an existingmonitoring system, can connect to an installed, in-place customer basequickly, with minimal local installation.

[0096] Referring to FIG. 5, an exemplary Internet-based energymanagement system of three buildings 10, 10A, 10B, according to theinvention, may be seen. It will be appreciated that the invention may beused with more or less than three buildings. Each respective building10, 10A, 10B has located on-site respective energy-using devices 11,11A, 11B. A preferred embodiment is discussed in which a building suchas building 10 has multiple energy-using devices 11.

[0097] Each respective building 10, 10A, 10B has associated therewithrespective meters 12, 12A, 12B. A preferred embodiment is discussed inwhich a building such as building 10 has multiple meters 12, but it ispossible for a building to have only one meter. As a meter may mentionedany metering device that measures energy-relevant information, such asair temperature, air quality, humidity, etc. Meters 12, 12A, 12B anddevices 11, 11A, 11B are connected through a building management systemor energy management system (such as an existing building managementsystem) and a network 15 (such as the Internet) to at least oneintelligent agent, most preferably to a system including intelligentagents. Each respective building 10, 10A, 10B has associated therewith arespective building management system or energy management system 13,13A, 13B (such as a conventional building management system, aconventional energy management system, etc.). In FIG. 5, a three layeredarchitecture (user interface, business logic, and data layer) is shown.

[0098] Each respective building 10, 10A, 10B has associated therewith arespective protocol driver 14, 14A, 14B. Each respective protocol driver14, 14A, 14B is in communication with a network 15 (such as theInternet). The network 15, in addition to receiving data from protocoldrivers 14, 14A, 14B, also receives other energy-relevant data 16 (suchas a price feed (in $/MWh) and/or a NOAA weather feed, etc.).

[0099] In this example, the network 15 (such as the Internet) further isin communication with a communication layer (such as a communicationlayer comprising AEM/DCOM (or other Engage Data Server driven by ActiveServer Page Technology through the firewall generating HTML pages) 17,FTP (File Transfer Protocol) 18, BacNet/UDP 19, and expandable protocolslots 20). BacNet/UDP is an open standard, an example of an openbuilding intercommunications protocol, put forward by the BacNetconsortium. BacNet via UDP takes that protocol and transports it indatagrams (UDP) over the Internet. The UDP is the envelope; the BacNetmessage is the content. A communication layer other than a communicationlayer comprising AEM/DCOM 17, FTP 18, BacNet/UDP 19, and expandableprotocol slots 20 may be used in the invention. It will be appreciatedthat AEM/DCOM, FTP, BacNET/UDP and expandable protocol slots are shownas examples and their use is not required, with communications toolsbeing useable in the invention.

[0100] AEM/DCOM 17, FTP 18, BacNet/UDP 19, and expandable protocol slots20 are included in data processing system or computer system 25. Dataprocessing or computer system 25 thus is able provide a real-timedatabase 26. The real-time database 26 advantageously includes real-timeenergy-relevant information specific to the buildings 10, 10A, 10B aswell as real-time energy-relevant information “from the world,” i.e.,the energy-relevant information 16 (such as price feed in $/MWH and NOAAweather feed).

[0101] Data processing or computer system 25 further includesintelligent agents 21, optional but particularly preferred financialengine 22, optional but particularly preferred notification workflowsystem 23 and optional but particularly preferred energy monitoringsystem 24, which receive, process and/or act on information communicatedvia the network 15 (such as the Internet). Advantageously, real-timereceipt is made possible, as well as real-time processing and/or actingon received information. The intelligent agents 21 are the heart of theintelligent use of energy system of FIG. 5. The intelligent agents 21preferably function in neural networks, which monitor each piece ofequipment, forming a non-parametric model of its behavior, allowingaccurate predictions of the impact that specific energy control actionswill have on the building environment. Also, energy savings predictionscan be accomplished based on environmental changes (temperature,air-quality, etc.). These “device agents” are used by higher-levelagents to pursue a number of strategies, such as “minimum disturbanceload rotation” or “supply air reset”. These intelligent agents functionlike highly specialized, 24×7 staff members, and can be switched on oroff, or given different goals to accomplish. The intelligent agents 21monitor and control the devices to maximize energy savings, whileminimizing impact on environmental quality.

[0102] The data processing or computer system 25 thus monitors andprocesses the real-time database 26, based on rules and/or parameters,and formulates real-time queries (such as queries for energy curtailmentpossibilities from energy-using devices 11 within building 10) and/orcommands (such as an energy curtailment round-robin rotation to beimposed on devices 11, 11A, 11B). The real-time queries and/or commandsformulated by the data processing or computer system 25 are communicatedin real-time via the network 15 (such as the Internet) to the respectiveprotocol drivers 14, 14A, 14B which leads to devices 11, 11A, 11B beingcontrolled in an overall energy use reducing manner but with minimizeddiscomfort or inconvenience to occupants or users of buildings 10, 10A,10B. Discomfort or inconvenience to occupants or users of buildings 10,10A, 10B is considered and included in the data processing or computersystem 25 so that a particular energy-using device in the plurality ofdevices 11, 11A, 11B will not be curtailed in its energy use in a mannerthat would cause discomfort or negative impact. Thus, certainenergy-using devices (such as computer equipment, hospital equipment,etc.) are treated differentially and intelligently so as not to besubjected to energy curtailment in the same manner as other energy-usingdevices, while other energy-using devices that are otherwise identicalbut in different buildings may be subjected to different energycurtailment based on time of day and occupancy or the like in therespective buildings. Thus, if building 10 and building 10A are indifferent time zones but otherwise have a similar set of respectivedevices 11, 11A, they may be controlled appropriately and in a maximallyenergy-intelligent manner.

[0103] It will be appreciated that the data processing or computersystem 25 depends on rules and/or expressions and/or logic which areexpressed in terms of variables and/or input which are manipulable andevaluable. For example, there may be used rules, expressions, variablesand/or input suitable for aggregating energy use for the entire systemof buildings 10, 10A, 10B and monitoring whether movement towards a newenergy peak is occurring.

[0104] Thus, it will be appreciated that, in operation, the computer ordata processing system 25 with the real-time database 26, the network 15(such as the Internet) and the buildings 10, 10A, 10B essentially runthemselves without necessity of a human operator. It will be appreciatedthat the computer or data processing system 25 can be far more effectiveat computational operations than can a human operator, and also canprocess the available data and real-time information, using the rules,far more quickly and accurately than a human operator could in the sameamount of time. Thus, the invention advantageously providesmachine-based operations in areas where reliance on human operatorsconventionally meant responses that now can be seen as relatively slow,inadequate or non-optimal.

[0105] Artificial intelligence and neural network technology are used sothat a controller for an energy using device such as protocol driver 14,for example, may have a basis for responding to a query for energycurtailment possibilities. A set of rules is put into place for theprotocol driver 14 and any energy-using devices 11 associated therewith.The set of rules is any set of rules appropriate to the energy-usingdevice, the building in which the energy-using device is situated, andthe building occupants or those served by the building. For example, theset of rules may take into account outside temperature, insidetemperature, etc. and based on the differential therebetween maycharacterize the comfort level, with certain differential ranges beingassigned to certain comfort level characterizations. While the set ofrules is fixed in operation, the set of rules may be subjected tooverhaul and change, such as if it is decided that a colder or warmertemperature range is now to be considered acceptable than in the past.While in a preferred embodiment the variables and rules operate so as tominimize any need or desire for human operator intervention, optionally,a manual human operator override may be provided, in which a humanoperator would be permitted to override computer-based control of one ormore energy-using devices.

[0106] Referring to FIG. 5, it will be appreciated that the invention asdiscussed above advantageously permits systems of buildings 10, 10A, 10Bto “run themselves” without the necessity of intervention of a humanoperator (on-site of buildings 10, 10A,10B or elsewhere such as at amonitoring facility). A human operator is not needed for making energycurtailment and energy use decisions and optimizing energy use inreal-time.

[0107] For the intelligent use of energy system of FIG. 5, buildings 10,10A, 10B and users (such as building managers, energy managers,financial managers, etc.) are connected over a network 15 (such as theInternet). The intelligent use of energy system may include modulesdedicated to meeting respective needs of users with differentresponsibilities and concerns, such as a financial engine module, anotification workflow module, an energy monitoring module, etc.

[0108] Moreover, valuable information is provided that buildingmanagers, financial managers and/or energy managers may observe how thecomputer-based system is performing, via browser-based user interface27. For example, in a preferred embodiment, users access the intelligentuse of energy system through a web-browner that connects to an ASPhosting site of intelligent use of energy software, which in turn,connects through the Internet, either directly or indirectly, to thebuildings 10, 10A, 10B managed by the system. To accommodate thediversity of building management systems and associated protocols thatare commercially in use, there may be used a communications module,preferably one that is an expandable communications bus architecturethat can easily accommodate new communication protocols as plug-ins;also, preferably the communications module is one that can communicatewith existing bidding management systems, “monitoring” systems andassociated protocols currently available in the marketplace as well asable to communicate with new systems being developed and developed inthe future. A particularly preferred communications module to use isIUE-Comm, developed by the present applicant

[0109] Building managers, financial managers, energy managers, and/orothers via browser-based user interface 27 may view information thatwould be of interest to them. For example, a building manager may usethe system of FIG. 5 to monitor the current state of devices 11, 11A,11B in the buildings 10, 10A, 10B. Building managers can see thetemperature setting of air-conditioners, the consumption of chillers,the speed of fans, etc. The building managers can also optionallysimulate one or more “what if” scenarios, using the intelligent agents,to predict the effect of control actions on the energy bill and thecomfort in the building. Building managers optionally may manipulate theparameters of the intelligent agents, such as by constraining thetemperature band used by a “supply air rest” agent. The building managerno longer needs to control individual devices (as he wouldconventionally do) because the intelligent use of energy system of FIG.5 is “goal based”. The manager gives the system a goal (such as to save40 KW in the next two hours) and the intelligent use of energy system ofFIG. 5 determines how to best achieve the goal. A building manager canrely on and use the intelligent agents like highly specialized, 24×7staff members, switching them on or off, or giving them different goalsto accomplish.

[0110] The energy manager refers to a human responsible for the optimaluse of energy across facilities, such as across buildings 10, 10A, 10B.Issuing curtailment requests, for instance, is one of the major tasks ofan energy manager. Using the system of FIG. 5, issuance of a curtailmentrequest optionally can be accomplished manually, or automatically bypre-instructing the intelligent agents.

[0111] The financial manager is a human. In a preferred embodiment ofthe invention, the financial manager generally is interested in showingthe savings that have been produced by using an intelligent use ofenergy system such as that according to FIG. 5. Finance modules in thesystem draw on a data warehouse that is created based on the system'sreal-time data base, and support the financial manager in analyzingenergy consumption, identifying peak demands, pin-pointing inefficientequipment or operations, and demonstrating the overall effect of theagents saving energy costs. While such mentioned finance-relatedactivities may not be necessary, they are particularly preferred forusing the invention in a commercial context.

[0112] The real-time database 26 will be understood as a databasecontinuously changing to reflect current data. The data in the real-timedatabase 26 preferably is saved in a data warehouse (not depicted onFIG. 5), and from the data warehouse is usable such as for energyanalysis and financial reporting.

[0113] A particularly preferred example of an energy curtailmentregiment that may be automatically devised and implemented according tothe invention is a round robin load rotation. The flow chart of FIG. 6shows a preferred round robin algorithm for load rotation. Forsimplicity, not all checks (e.g., equipment status, manual override,etc.) that may preferably be performed are shown on FIG. 6. In FIG. 6,the round robin algorithm begins with a curtailment call 600 for aspecific amount “X” KW (such as 30 KW). In response to the curtailmentcall 600, the rotation counter is set 602 to group curtailment duration,from which the system increments group selection counter 603. Also,based on the rotation counter being set 602, the system shuts groupequipment down 604. After group equipment shut down 604, the systemturns on previous equipment group 606. The system then asks 608 whetherthe curtailment requirement has been met, and if not, shuts the next(group+1) equipment down 609, and loops to re-ask 608 whether thecurtailment requirement has been met. The loop continues until thequestion 608 of whether the curtailment requirement has been met isanswered affirmatively, and then the system asks 610 if the rotationduration is complete; if not complete, the loop to the question 608 ofwhether the curtailment requirement has been met continues. When thequestion whether the rotation duration is complete 610 can be answeredaffirmatively, the system then asks 612 whether all groups have beenrotated through. If all groups have not been rotated through, return isprovided to incrementing group selection counter 603.

[0114] When the question 612 whether all groups have been rotatedthrough is answered affirmatively, the system next resets groupselection counter 614, and asks 616 whether the curtailment call iscomplete. If the curtailment call is not complete, i.e., if not enoughenergy curtailment can be achieved, the algorithm ends (END) on FIG. 6.

[0115] The invention has many practical and industrial uses. Forexample, the invention advantageously combines the power of artificialintelligence with the Internet to enable energy-using customers (such asbuilding systems) to dramatically cut building energy costs in realtime. Customers are able to do so by reducing the energy they consumeeach month, lowering their “peak load” demand charges, and byaggregating multiple electric meters into one “virtual meter.” Also, theneural network and artificial intelligence used in the invention permitmany factors that influence consumption (such as current weatherconditions, occupancy levels and market price of energy) to be takeninto account, as the system constantly monitors and adjusts energy use.

[0116] It is calculated that certain installations using the inventionare expected to be able to reduce energy costs by more than 15% when allmajor energy-consuming devices are connected and full control overdevice settings is given to a software system according to theinvention.

[0117] In another embodiment, the invention also provides forquantification of economic and energy savings, and for revenuegeneration. Particularly, the “curtailable” capacity and energy that maybe generated from using the invention may be sold to regional controlauthorities and/or energy service providers.

[0118] The invention takes energy management to a new level, by applyingthe power of artificial intelligence, and by drastically reducing orremoving the human element altogether (except when building managerschoose to over-ride the system). Neural network and intelligence agenttechnology is used to monitor, analyze and adjust energy consumption inreal-time. A number of factors, including energy prices, current andforecasted weather conditions, current and scheduled occupancy levels,space air temperature and space air quality, etc., may be taken intoconsideration. These factors are applied to the selection of strategiesfor reducing energy consumption at any given moment. The inventiveenergy management system is much more “intelligent” on a real-time basisthan a human operator, who could not possibly analyze all of theconstantly-changing factors affecting energy consumption and makeadjustments quickly. Furthermore, the system provides a wealth of newdata to building owners and managers, who can then make informeddecisions for further energy reductions and future equipment purchasedecisions.

[0119] The invention thus may be used to provide one or more of thefollowing advantages: permanent load reduction; peak load avoidance;aggregation of multiple meters under a single “virtual meter”; automatedcurtailment response to Independent System Operator (ISO)/supplierrequests; extensive baseline analysis, reporting and financial control;real-time reading of meters and devices; meter equipment trending;alarming; reporting; intelligent use of energy financing. Theseadvantages and uses of the invention are discussed as follows.

[0120] As for permanent load reduction, it will be appreciated that theinvention permits customers (such as industrial, commercial, university,hospital and other customers) to reduce their energy consumption on anongoing basis by making thousands of minor adjustments hour-by-hour,twenty-four hours a day, to every piece of equipment attached to thesystem. When hundreds of energy-consuming devices such as air handlers,chillers and lighting systems are covered by the system, minoradjustments to each one can have a significant impact on overall energyconsumption. For example, the system can meet energy reduction goals byraising the temperature in unoccupied rooms from 70 degrees to 75degrees. Or, in response to an unusually cool summer day, the systemmight decide that starting the air conditioning before employees arrivefor work would not be necessary or economic. Over the course of a monthor a year, these minor adjustments add up to significant reductions inenergy consumption and costs, without any discernable impact onoperations or people.

[0121] Peak load avoidance is also advantageously provided by theinvention. The energy bills for commercial customers consist of twoparts—the cost of total energy consumption for the month, and a chargefor the “peak” energy consumed during that month. This “peak load”charge can account for as much as 50% of the electric bill. Theinvention provides for intelligent use of energy, by applying artificialintelligence to achieve on-going peak load reduction guidelines, pre-setby the customer. In a typical situation, the customer would want toinsure that peak loads will not exceed a previously set maximum or, moreaggressively, might decide to reduce peak loads each month (such as by10%). At any point in the month when peak loads approach the presetthreshold, the intelligent agents in the invention can choose from awide variety of available strategies to prevent crossing the line, suchas raising (or lowering) thermostats throughout a building(s); dimminglights; etc. However, if executing a certain strategy would violateanother parameter or other parameters set by the customer (e.g., thattemperature must never go beyond a certain threshold or that lightscannot be dimmed below defined lumens, etc.), then the intelligentagents of the invention will either employ another strategy for reducingpeak load demand, or notify the customer that the goal cannot beachieved. All of this analysis, action and/or notification occurs withinminutes, and permits customers (including commercial customers inmulti-building systems) to truly control their peak load charges.

[0122] The ability to produce a virtual meter or virtual meters isanother advantage of the invention. Many commercial energy consumersreceive a bill for every meter in their building or portfolio ofbuildings. It is not unusual for a single building to have multipleelectric meters, and major office complexes or building portfolios in agiven area may have many meters. Beyond the inefficiency inherent inreceiving and paying numerous electric bills each month, electricityconsumers are also charged for multiple, separate peak loads. The totalof these peak load charges can be significantly greater than the actualpeak that a single consumer reached at a particular point in a givenmonth. Intelligent use of energy according to the present invention canresolve this problem by aggregating all of a customer's meters into one“virtual meter.” This virtual meter can encompass hundreds of meters indozens of buildings within a single Electricity (Energy) Pool. (TheUnited States is divided into ten Pools which have very different tariffstructures and regulations. As a result, a virtual meter cannotaggregate meters in different pools, under the present framework in theUnited States.) Customers could receive one bill, not hundreds, and thepeak load charge would be calculated against the combined meters, notagainst each individual meter. This can lead to significant savings.

[0123] Another advantage of the invention is the provision of automatedcurtailment. Solving the long-term energy problem in the United States(and elsewhere) will require a multi-dimensional approach. Newconstruction of energy plants and transmission facilities alone will notsolve the problem, particularly in the short term where California andother areas face the potential for a California-size crisis. The presentinvention can play a significant role in mitigating energy shortages,and over the long term, substantially reduce the need for and cost ofadditional energy infrastructure. Under the terms of many commercialcontracts, energy suppliers can ask customers to reduce consumption anagreed-upon number of times each year. During the electricity crisis inCalifornia in the spring of 2001, these provisions were invoked a numberof times. To encourage commercial consumers to reduce consumption, andthereby avoid a crisis like that in California, many energy suppliersoffered incentives to users to voluntarily curtail power during peakload events, such as unusually hot summer days. These incentives caninclude significant reservation charges for agreeing to be curtailed,discounts on tariffs, or even payments to companies that “sell back”unused power or capacity when requested to do so. In many instances,building managers used conventional energy management systems tomanually or non-analytically turn off equipment in response to requeststo cut consumption. In many cases, this led to the shutdown ofbusinesses for hours or days, as happened in California. The inventioncan prevent this undesirable business shutdown situation, byautomatically curtailing equipment in response to a request by theenergy supplier, and can do so in a manner that minimizes disruptions.The intelligent agents of the invention, for example, may be able toachieve the curtailment by slight adjustments in equipment, or byselectively shutting down non-essential devices first. Or, the system inthe invention may be set to shut down only non-essential buildings. Theinvention provides the ability to take into account many factors beforetaking action, and to do so within mere minutes of a request to curtailconsumption, something otherwise beyond the capability of any humanoperator or business manager or conventional energy management.

[0124] The invention also provides advantageous analyses, reporting andfinancial control. When a customer initially determines to proceed withstart-up of an inventive computer-based energy management systemaccording to the invention, the customer's data may be entered in thecomputer-based system and provide the baseline for future analysis ofthe customer's energy consumption. Thus the impact of the computer-basedsystem on the customer's energy consumption may be seen. Once acomputer-based energy management system according to the invention isfully operational, a customer is able to monitor and analyze its energyconsumption in real-time. Optionally a customer may customize an energymanagement system so that that it provides information in a manner andformat suited to the customer needs. A customer can monitor and analyzethe customer's energy consumption at a given moment or over anyspecified period of time.

[0125] Another use and advantage of the invention is regarding real-timereading of meters and devices. The energy consumption of every meter anddevice connected to the system may be monitored and evaluated, ifdesired. Equipment that is not performing at peak efficiency can berepaired or replaced, further lowering the overall energy costs. Theinvention thus provides a data stream relating to individual efficiencyof energy-using equipment.

[0126] The invention also is useful in meter equipment trending,including permitting energy-using customers to undertake trend analysison a meter-by-meter basis in real time. Building managers can accessscreens at any time that show the current usage trend on a given meter,and provide a forecast for future consumption if the trend does notchange.

[0127] The invention further provides for alarming, includingappropriate notification when any situation occurs outside specifiedparameters. For example, if a peak load threshold is about to beexceeded, the system provides notification immediately so that remedialaction can be taken. However, the system also provides notification forless critical problems, such as malfunction of a particular piece ofequipment, or sudden changes in energy consumption patterns.

[0128] In the invention, reporting may be provided. A full suite ofreports may be provided, which can be accessed at any time or on aregular basis. These reports may include billing rates and differentialbilling, load shaping and profiling, and virtually any other report thata client may specify.

[0129] In the present invention, advantages related to finance also maybe provided. Conventionally, the bill that an average commercialcustomer receives from its energy provider is immensely complex andfrequently incorrect. By using the invention, the customer may compareactual real time data collected against detailed baseline data andagainst rate and tariff structures that apply to the customer's energyconsumption. Such a comparison will show a customer the level of savingsand revenue achieved and help the customer to ascertain whether or notthe bill provided by the energy provider is correct.

[0130] Thus, the present invention provides the mentioned advantages,with a rapidity of evaluation and of execution, accuracy, and precisionwell beyond that possible by a human operator or team of humanoperators. Also, advantageously, the energy management systems of thepresent invention are intelligent and “learn,” i.e., the systems learnfrom prior experience to improve results over time.

EXAMPLE 1

[0131] Initial deployment of energy load reduction according to theinvention is accomplished by a fixed rotation schedule of equipment thatis stepped through in a serial fashion. System attributes, such asallowable curtailment duration and electrical demand, is determinedthrough functional testing and pre-programmed in a fixed matrix. Arotation script is then deployed to systematically cycle each piece (orgroup) of equipment off and on at a fixed duration. This ‘round robin’rotation approach offers a less-than-fully-optimized rotation cycle butthe system responses obtained from this method is used for training ofthe programmable intelligent agent (PIA) for optimal load rotation.

[0132] Ultimately, a programmable intelligent agent optimizes the loadrotation of curtailable loads, using a combination of intelligent agentswhich operate the device level, portfolio level, and pool level asfollows:

[0133] 1) Device Level Programmable Intelligent Agent—utilizes a forwardartificial neural network (FANN) to predict the load rotation period forequipment (device level IA).

[0134] 2) Portfolio Level Programmable Intelligent Agent—optimizesresource leveling based on the predicted load rotation periods derivedin the device level PIA. Initially the portfolio is defined as thebuilding revenue meter.

[0135] 3) Pool Level Intelligent Agent—optimizes the load rotation usingpre-conditioning strategies based on the timing of the market order.

[0136] Once the PIA algorithms are trained, the ‘round robin’ algorithmis withdrawn and the Intelligent Agent takes over schedule loadrotation.

[0137] The system to which this example is applied is as follows. Theequipment targeted for load rotation includes: electrical space heat,air conditioning compressors, fan motors, package unitary HVACequipment, and process motors.

[0138] The Intelligent Agent interface requirements include:

[0139] 1) Internet enabled on/off control. Curtailable loads are groupedinto “banks” of equivalent electrical demand. Multiple matrices withalternative demand sizes may be deployed.

[0140] 2) Optional Internet enabled monitoring of process limit(s)(required for resource leveling IAs). Where group on/off control isused, an average process measurement is to be provided, for example, theaverage space temperature or common return air temperature. If processlimit monitoring is not deployed, then an acceptable period of therotation schedule is determined through functional testing of processdrift.

[0141] 3) Optional Internet enabled control of process setpoint(required for preconditioning strategies IAs). If process setpoint isnot deployed, then preconditioning strategies cannot be implemented.

[0142] 4) Internet enabled monitoring of energy consumption through anyof the following methods:

[0143] a) Kw meter located either at the equipment level or in theelectrical service to the electric heaters.

[0144] b) Virtual Kw meter based on field verified rated kW andequipment on/off status. Calculation of virtual Kw can reside eitherwithin the customer's Building Control System or at an ApplicationServer.

[0145] A Rotation Schedule/Matrix example is shown in FIG. 7, of a roundrobin approach to load rotation. The rotation matrix in FIG. 6 providesan example grouping for a 30 KW curtailment rotation schedule.Curtailable demand is determined by continuous measurement using theInternet enabled kW meter.

[0146] A brief description of each data entry is as follows. Data fieldsin bold italic are temporary values which are updated once the PIAs forLoad Duration and Resource Leveling Optimization are deployed.

[0147] Rotation Group—Numerical group assignment for the purpose ofprioritization and counting. The rotation groups are fixed during theinitial deployment. The Load Rotation IAs optimize groups into equalload/equal duration.

[0148] Equipment ID—Alphanumeric equipment descriptor.

[0149] Controlled Device ID—Alphanumeric description of energy device.

[0150] Manual/Auto Indicator Address—The address of the Manual/AutoStatus. (The term “address” is used to refer to the location assigned bythe enabling platform (e.g., Silicon Energy “PtID”, Engagenet, etc.) tobe written to or read from.) Reading this point gives a “digital state”(0 or 1) indication the equipment has been placed in local override andis therefore not accessible for curtailment. If equipment status isunavailable, then the application “remembers” the outcome from theremote control event. For example, if equipment did not change kW duringprior remote control event, then digital state is set to off. (“0”).

[0151] Equipment Status Address—(Optional) A read only point thatindicates the operational status (state output) of a piece of equipment.Operational status includes: normal, alarm, alarm code.

[0152] Curtailable Demand Setpoint—The curtailable electrical demand(kW). The setpoint for resettable devices is derived by means of directmeasurement and approved by customer. For on/off devices, thecurtailable demand setpoint is equal to zero.

[0153] Curtailable Demand—Curtailable demand initially is fixed, derivedby means of direct measurement and review of normal kW process range.Once the Load Rotation IAs are enabled, the curtailable electricaldemand is calculated as:

Electrical Meter (prior time interval)−Curtailable Demand Setpoint.

[0154] The calculated curtailable Demand is used within the LoadRotation IAs to optimize groups into equal load/equal intervals.

[0155] On/Off Control Address—The primary control address to whichcurtailment on/off commands are written. The command is as detailed inthe on/off Control Command entry.

[0156] On/Off Control Command—The command signal to be written to theprimary control address to place the equipment into curtailment mode. Inmost cases, this is a digital state command (0 or 1). The complement ofthe state command is used to disable curtailment. In those cases thatrequire two cases, this typically is the “Internet control override”signal.

[0157] Reset Control Address—The (optional) secondary address to whichcurtailment commands are written.

[0158] Reset Control Command—The (optional) secondary command requiredto place a piece of equipment into curtailment mode. In most cases thisis an analog reset variable (%, mA, V), for example, a reduced speedinput into a variable speed drive. This input need not be reset todisable the curtailment mode, and is ignored when Control Command 1indicates disablement.

[0159] Minimum Equipment Off Time—The minimum duration in which a pieceof equipment may be sent into curtailment mode. Required to preventshort-cycling of equipment.

[0160] Maximum Curtailment Duration—The maximum duration a piece ofequipment may be sent into curtailment mode. This period is initiallydetermined through testing and is typically the worse case time intervalbefore a process limit (space temperature, CO₂, etc.) served by theequipment falls out of range. Once the Device Level IAs are trained, themaximum duration will be fed into the Load Rotation IA for sorting intoload groups.

[0161] Settling Duration—The time delay in which two equipment groupsare required to overlap in curtailment mode (both groups in curtailmentmode) before the prior group is brought out of curtailment mode. This isused to accommodate an overshoot electrical demand as the prior group'sequipment is brought back to non-curtailment mode. Initially thesettling duration will be fixed. Once the FANN IAs are trained, thesettling duration will be fed into the Load Rotation IA for determiningthe group overlap.

[0162] Revenue Meter Address—The address of the utility meter servingthe equipment to be placed in curtailment mode. The net desired kWcurtailment must be seen at the meters or additional groups must berotated into curtailment mode. Reasons for shortfalls in curtailment mayinclude equipment off or in local override mode, or other equipmentbrought online during the previous group rotation. A range isestablished for the need for additional curtailment.

[0163] Process Variable 1 Address—The address of the first processvariable to be used to constrain the magnitude and duration of thecurtailment. For example, an AHU load rotation may be constrained byspace temperature. This is typically be a zone temperature for AHU typecurtailment. This information may not be required for the simplest formof the Round Robin approach. Up to three process variables are availablefor use; while not required, at least preferably one is be used (examplefor no process variables: fountain pumps).

[0164] Process Variable 1 Min Range—This is the lower allowable limitfor the primary process variable. Note that a curtailment range istypically more extreme than for normal allowable operating conditions.

[0165] Process Variable 1 Max Range—This is the maximum allowable limitfor the primary process variable. Note that a curtailment range istypically more extreme than for normal allowable operating conditions.

[0166] Process Variable 2 Address—The address of the second processvariable to be used to constrain the magnitude and duration of thecurtailment. This information may not be required for the simplest formof the Round Robin approach. Two process variables are available foruse; while not required, at least one preferably is used (example for noprocess variables: fountain pumps).

[0167] Process Variable 2 Min Range—This is the lower allowable limitfor the secondary process variable. Note that a curtailment range istypically more extreme than for normal allowable operating conditions.

[0168] Process Variable 2 Max Range—This is the maximum allowable limitfor the secondary process variable. Note that a curtailment range istypically more extreme than for normal allowable operating conditions.

[0169] AND Inclusions—This entry lists equipment that must be placedinto curtailment mode simultaneously with the listed equipment entry.Circumstances for this include matched Supply Air/Return Air fan sets.

[0170] OR Exclusions—This entry lists equipment that cannot be placedinto curtailment mode simultaneously with the equipment entry.Circumstances for this include shutting down all elevatorssimultaneously.

[0171] In this example, data sources and grouping rules are as follows.Curtailment duration and impact are determined through functionaltesting; the equipment is disabled and the spaces served by the units ismonitored to determine the maximum duration of curtailment before spaceconditions fall out of acceptable range. Equipment is grouped under thefollowing rules:

[0172] Equipment must be controllable and curtailable.

[0173] Equipment with fixed and coinciding operational schedules (duringpossible periods of curtailment) are required.

[0174] Curtailment durations are determined by monitoring spaceconditions during functional testing. Space temperature is monitored atminimum; other conditions such as IAQ (CO₂ levels) or relative humiditylevels may also be observed to determine curtailment durations.

[0175] Equipment is grouped such that the curtailable demand for eachgroup is approximately equal.

[0176] The functional curtailment duration for each group is thesmallest duration for any individual piece of equipment within thatgroup.

[0177] Minimal occupant impact is the basis for group priority. Example:fountains first, alternating elevators next, AHUs after.

[0178] Simultaneous, multiple group curtailments are spread across thebuilding portfolio; concentrations of curtailment groups within onebuilding are not permitted.

[0179] Groups may require exclusivity. Example: all elevators may not bein the same group.

[0180] Groups may require inclusivity. Example: return and supply fanoperation for the same space may be interlinked.

[0181] The flow chart of FIG. 6 is applicable to the above rotationschedule example, for a 30 kW curtailment call. Additional equipmentstatus checks, manual override checks, etc. are performed that are notshown on FIG. 6.

EXAMPLE 2

[0182] An example of Peak Load: Virtual Meter according to the inventionversus Real Meters is shown in FIG. 8. In this hypothetical example, thecombined total energy usage recorded by four meters A, B, C and D was 95kW. However, Meter A reached its peak at 4:00 p.m. on the third day ofthe month, Meter B's peak occurred at 10:00 a.m. on the 12^(th), Meter Crecorded its highest usage at noon on the 16^(th) and Meter D recordedits peak at 6:00 p.m. on the 29^(th). Despite the fact that none ofthese peaks occurred at the same time, or even on the same day, thecustomer was charged for the combined total of the four.

[0183] With a virtual meter, however, there is only one recordedpeak—the single point in time during the month when the customer's totalaggregate usage was highest. In this example, that peak was only 80 kW,and could have occurred at any time during the billing cycle. Thisfunctionality can provide the customer the ability to negotiate with itsenergy supplier for a different rate or tariff and hence significantlylower the “peak load” bill. (It is noted that relations betweencommercial consumers and energy suppliers vary greatly, depending onwhether a given market is still regulated or deregulated. The ability ofa customer to win lower rates through negotiations will be highlydependent on the nature of the market and the players involved.)

EXAMPLE 3

[0184] In this Example, there is provided an energy management systemaccording to the invention in which are used five integrated products orfeatures:

[0185] 1) Permanent Load Reduction—software intelligent agents thatcontinually make and implement complex multi-input, device-settingdecisions, and permanently reduce the amount of energy consumed by acustomer.

[0186] 2) Peak Load Avoidance—The use of a neural network to forecast,identify and minimize peak load events, reducing the portion of acustomer's energy bill related to its peak energy usage each month.These peak load events can account for up to 50% of annual energy costsand thus their reduction is highly advantageous.

[0187] 3) Virtual Meter Data Aggregation—The integration of multiplebuildings and electrical meters into one virtual meter, which caneliminate multiple billings, consolidate billable peak loads and givethe customer greater flexibility in managing its energy consumption.This, in turn, can create a new, reduced peak load for the aggregatedportfolio that will allow for negotiations of better rates from thecustomer's energy supplier, thus notably reducing demand charges.

[0188] 4) Capacity Savings and Emergency Curtailment—The system has theability to rapidly reduce a customer's immediate energy usage at itsrequest in response to short-term curtailments in energy supply. Energyconsumption may be rapidly curtailed in response to requests byauthorities or the energy supplier. This capability also makes itpossible for a customer to sell into available markets the kilowatts ofcapacity it can curtail and the kilowatt-hours of energy it is able toprovide back to the market during an emergency. Upon request by thelocal ISO, power authority or utility supplier, the IUE system willplace buildings in peak curtailment operation. Non-essential loads willbe de-energized and HVAC equipment will be rotated in and out of serviceto maintain a consistent load reduction through the curtailment period.

[0189] 5) Finance—Tools for comparing actual energy usage with energybills, which frequently overstate the amount of energy consumed bycommercial customers.

[0190] In the following Examples and Comparative Examples, a number ofscenarios illustrate the major issues in which ways to manage energyconventionally and according to the invention differ, and show theadvantage of using intelligent agents to manage energy.

Comparative Example C1

[0191] An automated building management system or energy managementsystem

Comparative Example C2

[0192] A building management system operated by an expert humanengineer.

Inventive Example 4

[0193] Intelligent agents are provided according to the invention. TheIntelligent agents continually learn. A “modeling neural net” isconnected to each device controlled. This net has one job: learn allthere is to know about this device. All parameters of this device arefollowed by the neural net. Minute changes in operating characteristics,due to wear and tear, aging, weather, new parameter constellations etc.are immediately picked up and become part of the “model” that the agentshave of this device.

[0194] For an air conditioner, for example, the neural network knows howmuch power it consumes at a certain temperature setting at a certainoutside temperature with a certain occupancy of the building. The netalso knows this connection from other perspectives, it knows at whichtemperature setting, for a given occupancy and outside temperature whatthe power consumption would be.

[0195] The relationships between these parameters is neither linear noreasily expressable in an algebraic formula or differential equation(which are the standard ways in engineering to model systems). Theneural networks work like the human mind, creating connections betweenconcepts, which are either reinforced or weakened, depending onobservation—herein called “learning”, as in how children learn languageor a ball game.

[0196] Continuous Learning

[0197] By knowing about every detail of operational characteristics, theintelligent agents can run the devices in the optimal fashion for theway the equipment operates right that moment. This characteristic iscontrasted this to the way a conventional, automated building managementsystem (BMS) works. Usually, the BMS does not even have a model of thedevice. The BMS may give fixed instructions to the device, regardless ofthe current efficiency or price of energy or interaction betweentemperature and occupancy. The BMS has put the device on a schedule, e.ggo to 71 F by 7 am, and that what it will do until it is given a newschedule. At best, the BMS has resets that are based on more than onevariable, e.g., outside air temperature or space air temperature. Yetthe BMS still does not make predictions about future energy use, whichwould inform its energy management decisions. BMSs are typically set forworst case ranges, to avoid trouble calls—clearly not the smartest ormost energy efficient way to run a building. It is easy to see howenergy is wasted by such a rigid operation as the Comparative Example 1,that does not take changes and idiosyncrasies at the equipment levelinto effect. Also the case (Comparative Example 2) where a human expertcontrols is building management system is not much better. It isimpossible for a human to monitor hundreds of devices with tens ofpoints on every device. Imagine a building engineer in front of amonitoring console who has to track 230 air conditioners, the energyconsumption of those devices, their temperature setting, the time ittakes each device to change by 1 degree Fahrenheit, do that at differentexterior temperatures, factor in different occupancy loads, and do that24 hours a day. Clearly the human operator is overloaded withinformation.

[0198] Accuracy of Forecasting

[0199] Building management system such as Comparative Examples 1 and 2are not used to forecast energy use. At best, these systems can look upyesterday's energy use and report that to a human user. They cannotfactor this information into their own control actions. At best,Comparative Examples 1 and 2 need a human to do this. The human buildingmanagers will base their actions usually on “experience”, meaning thatthey will look at the weather forecast and err on the save side, eitherovercooling a building in the summer, or overheating it in the summer.Building managers are mostly striving to please patrons, not financialmanagers. Even a cost-conscious building manager is lacking the inputsand the modeling power to create an accurate forecast for theirbuildings, device by device, floor by floor, building by building,campus by campus.

[0200] The intelligent agents of Inventive Example 4, on the other hand,leverage the learning that has occurred over time in the neural networkand use it to predict energy usage exactly in that fashion. Predictionsare made short term or long range device by device, building bybuilding, portfolio by portfolio. This allows the agents to pre-cooljust to the right amount. During a curtailment it allows agents topredict degradation of room temperature and gently rotate the equipmentthat is either being shut off or reduced. Thus the impact on buildingcomfort is maintained, while energy cost is kept at its lowest.

[0201] Flexibility of Responses to Curtailment Requests

[0202] The conventional way as in Conventional Examples 1 and 2 toimplement a curtailment response is to devise a number of curtailmentstages (e.g Normal Stage, Yellow Alert Stage, Red Alert Stage). Asenergy becomes more scarce the possibility of a rolling black-outoccurs, and higher curtailment stages are called into action. Each suchcurtailment stage specifies exactly how energy must be conserved.Detailed plans exist that require certain air conditioners to be set tohigher temperatures or to be switched off completely. Instructions forswitching off certain banks of lights or pumps or other such measuresmay be part of curtailment plans. One can easily see that these plans asin Comparative Examples 1 and 2 are very rigid. Clearly energy shortagesdo not come in three or four flavors. Yet the responses are patternedthat way. The reason for this is obvious: in order to deal with the vastcomplexity of energy consuming devices, certain simplifications must bemade to react quickly. Coarse tools, such as block-based buildingmanagement systems and human operators who can only execute a limitednumber of operations until the curtailment level is to be met,inherently provide such limitations.

[0203] Greater flexibility is provided by the intelligent agents as usedin Inventive Example 4. The agents can monitor energy price and scarcityof energy in the grid. This can happen on a extremely fine grainedscale, not just green-yellow-red. Due to the agents ability to reason,they can react most appropriately even in early stages of an energycrunch. As the crunch gets more severe, agents can adopt theircurtailment measures too. These curtailment measures are not the coarsemeasures taken be switching blocks of equipment off but try to minimizethe impact on comfort and quality in the building. The agents can dothis by using their knowledge about the operating characteristics of thedevices and by forecasting energy need and consumption. Then the agentscan take gentle control actions. While each control action may only savea minute amount of energy, compared to the sledge-hammer method ofcompletely switching of full banks of equipment, the sum of these manyminute savings equals the coarse action savings taken today by lesssophisticated control systems and overworked building managers.

[0204] Wider Range of Information Inputs

[0205] Building management systems (BMS) as in the Comparative Exampleuse simple feedback loops to control temperature or other suchvariables. The feedback/control variable in these loops is mostly asingle, internal parameter to the system. Such an approach is too myopicto manage a system intelligently. The controller does not take asufficiently wide range of variables into consideration. There areadditional internal variables from the BMS itself (such as carbondioxide or other air quality measures). Connected to this are occupancydate, influencing variables such as air flow or fan speed or heating inwinter. Humans rarely monitor global variables from outside the buildingthat influence major decisions which can impact cost immensely. Buildingmanagers usually do not have a display of the current price of energy.On those extreme days where the price of energy in unregulated marketsshoots over the $200/kWh mark, neither the BMS on a schedule, nor thehuman building manager will react to this fact. While the price ofenergy in a deregulated market is mostly of concern to the ESCO and notthe end-consumer, not preparing for flexible pricing falls short ofbeing prudent energy management for every participant.

[0206] Intelligent agents as in Inventive Example 4 on the other handtake many global variables into consideration. The agents thus have theability to aggressively conserve energy when it becomes extremelyexpensive. Thus the agents sacrifice a little building comfort when itpays heavy dividends, yet keep the building comfortable when it is cheapto do so. Knowing about events in the world of energy, and not just thelocal building, thus pays a return to the building owner.

[0207] Rigid Scheduling Versus Knowledge-Based Reasoning

[0208] Previously, the dynamic reasoning and decisions based onknowledge as in Inventive Example 4 have been said to be superior tofixed schedules or simple loops according to Comparative Examples 1 and2. Yet all conventional building management systems employ exactly thesebasic concepts. The schedule is the preferred way to control a device ina conventional system. Even though this schedule may be verysophisticated (such as being able to distinguish weekdays from weekends,to recognize holidays, to set repeats, very much like the calendarfunction in Mircosoft Outlook, for example), still the conventionalschedule does not ascertain whether the currently scheduled course ofaction makes sense under the current environmental conditions. Theconventional BMS will do what is instructed to do, even if it has longgone off-course. In the scenario of energy costs skyrocketing on aparticularly hot summer's day in California, the BMS will do what it isscheduled to do. It will cool the space to 68F, even if that runs up anenergy bill that is in the hundreds of thousands of dollars just forthat one day.

[0209] The agents of Inventive Example 4, on the other hand, knowing theenergy price, will start reasoning to find a compromise between cost andcomfort. First the agents will most likely set the temperature to 71F,then they will rotate among various zones in the building to distribute“discomfort” equally, and only when that is exhausted go to an evenhigher temperature to keep costs under control.

[0210] Feedback Loops

[0211] From a cybernetic point of view, current BMSs such as theComparative Example use either simple feedback loops (keep temperature aset level) or they use triggers from simple sensors to control an action(light sensor switches on parking lot lights). Such cyberneticconstructs cannot deal with diametrically opposed objectives, such ashow to save as much energy as possible while keeping building comfort ashigh as possible.

[0212] The Inventive Example 4 has the artificial intelligence tools tobalance opposed objectives, such as energy saving and building comfort.

[0213] Blocks

[0214] The Comparative Example building management system is created onthe metaphor of its predecessor—electromechanical systems. End-users arepresented with “blocks” which may represent relays and other suchphysical entities. On a higher level control blocks represent blocks ofpanels or devices. While this metaphor is initially helpful for abuilding manager to make the transition from the physical world tosystems controlled by microprocessors, it does eventually limits whatthe system can do.

[0215] Inventive Example 4 is not subject to such eventual limitations.

[0216] Trial and Error

[0217] Some BMSs and even some domestic thermostats claim they can“learn”. What most of these devices are actually doing is a stochasticapproximation approach—in other words: they are guessing. Guessing avalue can be very wasteful, taking many “stabs” until a somewhatsatisfactory value is found. Guessing also allows no transfer from onelearning event to the next. The Comparative Example suffers from thecosts of trial and error.

[0218] In Inventive Example 1, true learning (observation and thecreation of a knowledge base) occurs, such as by neural networks andrule-based expert systems. Due to such true learning, the system inInventive Example 1 therefore come up with the right answer faster, moreaccurately and in a wider range of learning situations than theComparative Examples.

[0219] The differences between the Comparative Examples 1 and 2 andInventive Example 4 are summarized as follows. Inventive Example 1 usingintelligent agents surpasses both, automated building management systems(Comparative Example 1) and human experts (Comparative Example 2) indelivering better building comfort at less cost. The table belowsummarizes these findings. Intelligent Agents Human Expert drivingConcept driving a BMS a BMS Automatic BMS Learning operational Constantobservation of Common sense of At best a fixed parametric specifics ofdevices every single device updates operational characteristics;description of a device; no [how to run air the neural network for thatmacro level only, can not learning or updating. coniditioners, pumpsetc. device continually; up-to- follow all points on all Usually nomodeling of most efficiently] date representation of the devices indetail for 24 device parameters. device hours per day. Forecastingenergy usage Neural nets can make Human can make educated At best lookupin a [saving money by doing accurate predictions based guess based onexperience; database, very inaccurate; necessary cooling/heating onhistoric observations. usually can not take all usually no forecasting.actions when energy is datapoints into account due cheap] to informationoverload and non-linear nature of forecasting formulas. Dealing withcurtailments Infinitely small levels of Brute force; using None; needshuman [keeping good building automatic response to predefined groups ofoperator. comfort while responding tightening energy supply; devices toswitch off in to curtailments] increasing stages of severity Day to dayoperation of Constant observation of Operates BMS on macro According toa fixed devices every single device updates level; can not dedicate fullschedule (e.g. set A/C to [Running the building in the neural networkfor that 8 hours to drive system; 70 F. at 6am) or a single the mostenergy efficient device continually; up-to- unable to deal with flow ofevent (e.g. parking lots manner] date representation of the information(up to light go on when it is device 150 Mb/minute) dark); no tacticaloptimization. Context for decision Use all local feeds from Not allglobal information Use local sensor readings making building (e.gtemperature, feeds accessible to building only for simple feedback[being pound-wise, not humidity, CO2, etc.); use manager; decisions madeloop penny foolish] weather forecasts from with incomplete data on aNOAA; use price feeds guesswork basis from ISOs; Decision making methodsForward and backward Intelligent decision making Simple feedback loopsreasoning using neural using reasoning and with single control networks,rule-based “common sense” parameter systems, and plan-based systems;Granularity of Control Every device has an agent Limited by the controlControlling via “blocks” attached which in turn can level provided bythe BMS and other electro- be leveraged by higher mechanical metaphorsthat level strategy agents; this do not utilize the full allows toimplement many flexibility of computerized different strategies, systemsindependent of physical “control blocks” Automatic determination Neuralnetworks Based on experience and Trial and error to deduce of parameterscontinually use monitoring observations; prone to pre-cooling orpre-heating as input to learning; system misjudgements and effects timeswastes energy and never guesses but uses of exponential effects does notadapt to changes reasoning based on historic (humans can only estimatein the building or the facts. linear systems well). environment(weather) Control over multiple Agents can leverage Restricted to asingle Restricted to a single buildings multiple devices and buildingvia the current building. If umbrella therefore have more BMS. systemexist than only for degrees of freedom in monitoring. finding the bestcompromise between savings and comfort

[0220] The basis of comparison is both, the cost of energy consumed inthe scenario and the comfort level achieved for the tenants.

[0221] The scenarios show that agents can run a building moreeffectively and efficiently due to the following reasons:

[0222] Continuous learning: Intelligent agents operate equipment moreefficiently and effectively by automatically learning the operatingcharacteristics of devices via a neural network approach is. This isfaster, more accurate, and more representative of the current state ofthe device than preprogramming a building management system with static,manufacturer supplied parameters or specs for the device, which may notrepresent the current condition of the actual device.

[0223] Accurate forecasting: Intelligent agents save money by usingtheir device knowledge to make knowledgeable energy forecasts whichresult in savings rather than operating on a fixed schedule regardlessof temperature forecasts or changing energy prices.

[0224] Flexible response to curtailment requests: Intelligent agents canachieve higher comfort levels for tenants during curtailment eventssince they do not rely on predetermined “shut down” groups or sequences,which are the only way building management systems or humans can handlethese complex requests for curbing energy use.

[0225] Wider information context: Since agents have access to a widercontext to base their decisions on, they can save more money than abuilding management system. The building management system isconstrained to data that comes from the building itself, while theagents can leverage subscriptions to the NOAA (national oceanographicand atmospheric administration) or to various price feeds from ISOs.

[0226] Reasoning wins over scheduling: Agents can run the equipment moreeffectively and efficiently on a day to day basis. The agents can dothis, since they have the ability to reason about causes and trade-offsreact flexibly to events in the building itself (temperature, CO₂,occupancy, etc.) and to global changes (weather data, price of energy).Building management systems are usually on a schedule, where they takecontrol actions, regardless of occupancy or the price of energy. Thismakes building management systems less efficient.

[0227] Reasoning is more powerful than feedback loops: From a cyberneticpoint of view, current BMSs use either simple feedback loops (keeptemperature a set level) or they use triggers from simple sensors tocontrol an action (light sensor switches on parking lot lights). Suchcybernetic constructs can not deal with diametrically opposedobjectives, namely to save as much energy as possible while keepingbuilding comfort as high as possible. It takes domain knowledge,learning, and decision making intelligence in a system to accomplishthis.

[0228] Conventional block control is cumbersome: Most buildingmanagement systems have been created on the metaphor of theirpredecessors—electromechanical systems. End-users are presented with“blocks” which may represent relays and other such physical entities. Ona higher level control blocks represent blocks of panels or devices.While this metaphor is initially helpful for a building manager to makethe transition from the physical world to systems controlled bymicroprocessors, it does eventually limits what the system can do.

[0229] Intelligent learning is more cost effective than trial-and-error:While some conventional BMSs and even some domestic thermostats claimthey can “learn”, what most of these devices actually are doing is astochastic approximation approach—in other words: they are guessing.Guessing a value can be very wasteful, taking many “stabs” until asomewhat satisfactory value is found. Guessing also allows no transferfrom one learning event to the next. True learning on the other handrequires observation and the creation of a knowledge base. Neuralnetworks and rule-based expert systems can do this and therefore come upwith the right answer faster, more accurately and in a wider range oflearning situations.

[0230] Agents control more than one building: While traditional BMSsmerely schedule and monitor the actions in a single building, our agentscontrol devices, such as HVAC or lighting across a whole portfolio ofbuildings. This prevents the agents from merely finding local maxima butallow them to globally optimize. It also equips the agents withincreased degrees of freedom in balancing energy savings requirementswith tenant comfort across buildings.

[0231] While various embodiments of the present invention have beenshown and described, it should be understood that other modifications,substitutions and alternatives may be made by one of ordinary skill inthe art without departing from the spirit and scope of the invention,which should be determined from the appended claims.

What we claim is as follows:
 1. An energy management system comprising:computer-based monitoring for an adverse energy event in a buildingsystem; computer-based recognition of an adverse energy event in thebuilding system; immediate automatic querying of energy users within thebuilding system for energy curtailment possibilities; automatic receiptof responses from queried energy users with energy curtailmentpossibilities; automatic processing of energy curtailment possibilitiesinto a round-robin curtailment rotation.
 2. The system of claim 1,wherein the adverse energy event is selected from the group consistingof a new peak demand; a human-given directive to curtail a certainamount of energy consumption; and an excess increase of energy price ina deregulated market.
 3. The system of claim 1, wherein the monitoringoccurs in a context selected from a business-as-usual context; 24×7permanent load reduction context; and an emergency context.
 4. Thesystem of claim 1, including 24×7 permanent load reduction.
 5. Thesystem of claim 1, including minimization of energy consumption inongoing business-as-usual energy consumption.
 6. The system of claim 1,wherein the building system is selected from the group consisting of asingle building and at least two buildings.
 7. A system of claim 1,wherein the adverse energy event is a surge or a steady increase towardsa new peak demand.
 8. A system of claim 1, wherein the system comprisesat least two buildings.
 9. A system of claim 1, wherein the systemcomprises load balancing between buildings.
 10. The system of claim 1,wherein served by the system is a building or are buildings selectedfrom the group consisting of at least one university building; at leastone hotel building; at least one hospital building; at least one cardealership building; at least one shopping mall; at lease one governmentbuilding; at least one chemical processing plant; at least onemanufacturing facility; and any combination thereof of buildings. 11.The system of claim 1, wherein at least two buildings are undermanagement and are geographically dispersed.
 12. The system of claim 1,wherein a human operator is not needed.
 13. The system of claim 12,wherein a human operator has an optional override right.
 14. The systemof claim 1, including at least two buildings, said buildings beingcommonly owned or not commonly owned.
 15. The system of claim 1,including automatic documentation of energy savings attributable to anyautomatic intervention(s) by the energy management system.
 16. A methodfor minimizing and/or eliminating need for human operator attention inenergy management of a building system, comprising: non-human,computerized processing of obtained energy data, wherein the obtainedenergy data is for at least one energy user in the building system, saidprocessing including (A) automatic determination of whether at least oneenergy-relevant event is present or (B) continual optimization of asetting of the at least one energy user.
 17. The method of claim 16,wherein the energy-relevant event is a threat of a new maximum peak. 18.The method of claim 17, wherein the peak is selected from the groupconsisting of a kW demand peak, a lighting peak, a carbon dioxide peakand a pollutant peak.
 19. The method of claim 16, including, when aenergy-relevant event is automatically determined to be present,immediately activating an automatic response to the energy-relevantevent.
 20. The method of claim 19, wherein the automatic response isnon-determinative.
 21. The method of claim 17, wherein at least oneintelligent agent, from the obtained energy data, actually forecasts thepeak.
 22. The method of claim 19, wherein the energy-relevant event is athreat of a new maximum peak, and the immediately activated automaticresponse includes energy reduction interventions to avoid the newmaximum peak.
 23. The method of claim 16, wherein the automaticdetermination of whether at least one energy-relevant event is presentcomprises application of artificial intelligence.
 24. The method ofclaim 23, wherein the artificial intelligence is selected from the groupconsisting of neural networks; rule-based expert systems; and goal-basedplanning systems.
 25. The method of claim 16, wherein more obtainedenergy data is processed in a given time period than could be processedby a human being.
 26. The method of claim 16, wherein the buildingsystem comprises at least two buildings.
 27. The method of claim 16,including machine-based learning from the obtained data and/ormachined-based constructing a model from the obtained data.
 28. Themethod of claim 16, wherein the building system includes a building orbuildings selected from the group consisting of at least one universitybuilding; at least one hotel building; at least one hospital building;at least one car dealership building; at least one shopping mall; atlease one government building; at least one chemical processing plant;at least one manufacturing facility; and any combination thereof ofbuildings.
 29. The method of claim 16, wherein at two least buildingsare under management and are geographically dispersed.
 30. The method ofclaim 16, wherein a human operator is not needed.
 31. The method ofclaim 16, wherein the building system includes at least two buildingsand the at least two buildings are commonly owned or not commonly owned.32. The method of claim 16, including automatic documentation of energysavings attributable to any said automatic intervention(s).
 33. Themethod of claim 16, including machine-based reasoning to select betweenat least two conflicting goals.
 34. The method of claim 33, wherein themachine-based reasoning is to select between a market price goal and acomfort-maintenance goal.
 35. The method of claim 16, including acomputerized display of energy data and/or device.
 36. The method ofclaim 16, including, on human demand, computerized forecasting;computerized simulation of an effect or effects of a proposed controlaction; and/or computerized reporting on simulation at various levels ofaggregation.
 37. The method of claim 36, wherein the aggregation levelfor the computerized reporting is at an individual device, at everythingin a building, at a set of buildings, or everything commonly owned. 38.A computer-based energy management system, comprising: (A) non-human,computerized processing of obtained energy data, wherein the obtainedenergy data is for at least one energy user in a building system, saidprocessing including automatic determination of whether at least oneenergy-relevant event is present; and (B) upon recognition of anautomatic determination that at least one energy-relevant event, anon-human, computerized response thereto based upon artificialintelligence reasoning.
 39. The system of claim 38, wherein thenon-human, computerized response is formulated after processing of moreinformation than could be accomplished by a human in whatever processingtime has been expended.
 40. The system of claim 38, including artificialintelligence reasoning based on one or more of: (A) knowledge about abuilding or buildings in the building system; (B) knowledge about anenergy using device; (C) knowledge about the building system; and (D)data outside the building system.
 41. The system of claim 38, includingautomatic querying of energy users.
 42. The system of claim 41,including receiving responses from queried energy users andautomatically processing the received responses.
 43. The system of claim42, including automatic formulation of an optimal energy-saving commanddecision and/or strategy.
 44. The system of claim 43, includingexecuting the optimal energy-saving command decision or strategy. 45.The system of claim 44, wherein the optimal energy-saving commanddecision comprises a rotation of energy curtailment that minimizesimpact over energy users in the system.
 46. The system of claim 38,wherein the computerized response includes at least one determinationbased on one or more of: (A) air quality, humidity, pollutants, air flowspeed, temperature, and other descriptors of physical properties of air;(B) light direction, light color, ambient temperature, foot candle, kwconsumption of light producing equipment, smell of light, and otherdescriptors of physical properties of light; (C) plug load; motionsensed by motion sensors; carbon dioxide levels; brightness; soundlevels; automated device for sensing human presence; motion detectors;light-sensing apparatus; habitation-sensor; (D) chemical or biologicalwarfare agent sensing device.
 47. The system of claim 46, wherein thechemical or biological warfare agent sensing device is selected from thegroup consisting of a mustard gas sensor, an anthrax sensor, a carbonmonoxide sensor, a carbon dioxide sensor, a chlorine gas sensor and anerve gas sensor.
 48. The system of claim 38, wherein the artificialintelligence reasoning comprises at least one artificial intelligentagent and at any given time, what the artificial intelligence agent isdoing may be monitored.
 49. The system of claim 47, including monitoringis by a human viewing what the artificial intelligence agent is doing.50. The system of claim 47, including generation of a log of historicalactivity by one or more artificial intelligent agents performing theartificial intelligence reasoning.
 51. The system of claim 38, includingmachine-based detection of presence of a chemical or biological warfareagent, to which a machine-based response is determined.
 52. The systemof claim 48, wherein the machine-based response includes release of ananti-agent and/or adjustment of one or more energy users.
 53. The systemof claim 38, including at least one machine-based determination of atleast one parameter of interest to a building manager, said parameterbeing measurable and controllable.
 54. The system of claim 38, includingautomatic monitoring of the computerized response.
 55. The system ofclaim 38, including communication over the Internet.
 56. The system ofclaim 38, wherein a human operator may enter a query.
 57. The system ofclaim 56, wherein the human operator query is a query as to currentstate of one or more devices in a specified building.
 58. The system ofclaim 56, wherein the human operator query is a query requesting aprediction of effect of proposed control action(s) on an energy billand/or on comfort.
 59. A computer-based round-robin rotation system forenergy users, wherein the energy users are under computer-based controland are present in a building system, the round-robin rotation systemcomprising a series of computer-based energy curtailment commands toeach of a plurality of energy users in the building system, wherein (1)each computer-based energy curtailment command in the series of energycurtailment commands (a) is specific to the energy user to which thecurtailment command is directed; (b) has been derived from an energycurtailment offer provided by the energy user; and/or (c) is based oncontinually learned and observed characteristics of the energy user;and/or (2) an energy user in the plurality of energy users is groupedwith other energy users based on similarity with regard to a certainparameter or parameters.
 60. The round-robin rotation system of claim59, wherein the building system includes at least two buildings.
 61. Theround-robin rotation system of claim 59, wherein the round-robin systemis formulated in response to a human request for energy curtailment. 62.The round-robin rotation system of claim 59, wherein the round-robinsystem is implemented under business-as-usual circumstances.
 63. Theround-robin rotation system of claim 62, wherein the system has learnedby artificial intelligence that a desired target parameter in each areaserved by the system can be maintained by a round-robin rotation. 64.The round-robin rotation system of claim 63, wherein the targetparameter is room temperature.
 65. A computer based method of avoiding anew energy peak, comprising: priming a computer-based system with dataas to energy peak(s) already reached in a building system; for currentenergy usage in the building system, obtaining, in real-time,computer-readable data from which to automatically forecast if a newenergy peak is approaching; and real-time automatic processing theobtained computer-readable data to forecast whether or not a new energypeak is approaching.
 66. The computer-based method of avoiding a newenergy peak of claim 65, wherein the building system comprises at leasttwo buildings.
 67. The computer-based method of claim 66, includingcompiling a complete array of historical data in computer-readable form,determining one or more patterns thereform, and comparing therewithcurrent real-time data to forecast if a new peak is going to be reached.68. The computer-based method of claim 66, including neural networkbased prediction.
 69. The method of claim 65, wherein, if the real-timeautomatic processing of the obtained computer-readable data provides aforecast that a new energy peak is approaching, initiating an immediate,real-time, automatic response.
 70. The method of claim 69, wherein nohuman operator intervention is involved in either the automaticprocessing to forecast whether or not a new energy peak is approachingnor the immediate, real-time, automatic response to the forecast that anew energy peak is approaching.
 71. The method of claim 69, wherein thecomputer-readable data from which to automatically forecast if a newenergy peak is approaching comprises data from the energy users in thebuilding system.
 72. The method of claim 69, wherein thecomputer-readable data is from a source selected from the groupconsisting of sensing devices; electric meters used for billing; andinformation from individual devices.
 73. The method of claim 69, whereindemand for each individual device is forecast based on temperatureforecasts; patterns historically observed and learned via artificialintelligence and under continual update; and occupancy where theindividual device is provided.
 74. An energy curtailment systemcomprising an automatically managed round-robin rotation of a pluralityof energy curtailment interventions.
 75. The energy curtailment systemof claim 74, wherein each respective energy curtailment interventionwithin the plurality of energy curtailment interventions is derived froman energy curtailment offer from a to-be-curtailed energy user.
 76. Theenergy curtailment system of claim 75, including a plurality ofto-be-curtailed energy users in a multi-building system.
 77. The energycurtailment system of claim 76, wherein the multi-building systemincludes buildings geographically dispersed at least a state's distanceapart.
 78. The energy curtailment system of claim 75, wherein new energypeaks are avoided without human operator intervention.
 79. The energycurtailment system of claim 75, wherein automatic documentation isautomatically generated of avoidance of a new energy peak, with saidautomatic documentation being (a) stored in an accessible computer fileand/or (b) printed and/or stored in a human operator-friendly format.80. The energy management system of claim 1, wherein monthly energyconsumption is reduced for the building system and/or peak load demandcharges for the building system are lowered.
 81. The energy managementsystem of claim 1, including one revenue-grade virtual meter which is anaggregation of revenue-grade meters.
 82. The energy management system ofclaim 1, wherein energy use is constantly monitored and/or adjusted,said constant monitoring and/or adjustment being non-human, whereinbusiness-as-usual constant adjustment, 24×7 load reduction is provided.83. The energy management system of claim 82, wherein the non-humanconstant monitoring and/or adjustment is by artificial intelligence. 84.The energy management system of claim 82, wherein the non-human constantmonitoring and/or adjustment is to monitor and/or adjust at least onefactor that influences energy consumption.
 85. The energy managementsystem of claim 84, wherein the at least one factor that influencesenergy consumption is selected from the group consisting of currentweather conditions at and/or approaching an energy-user; occupancylevels of a facility served by an energy-user; market price of energy;weather forecasts; market price forecasts; air quality; air qualityforecasts; lighting quality; lighting quality forecasts; plug loadpatterns; and plug load pattern forecasts.
 86. The energy managementsystem of claim 85, including monitoring and adjusting based on all ofcurrent weather conditions at and/or approaching an energy-user;occupancy levels of a facility served by an energy-user; market price ofenergy; weather forecasts; market price forecasts; air quality; airquality forecasts; lighting quality; lighting quality forecasts; plugload patterns; and plug load pattern forecasts.
 87. The energymanagement system of claim 1, wherein the adverse energy event beingmonitored-for is at least one recognizable pattern of data that has beenlearned via artificial intelligence by a computer system doing themonitoring.
 88. The energy management system of claim 87, wherein thecomputer doing the recognition of an adverse energy event, for eachrecognized pattern of data that is an adverse energy event, reacts withan automatic response based upon reasoning.
 89. The system of claim 88,wherein the reasoning-based response is a querying response to beexecuted.
 90. The energy management system of claim 1, wherein responsesfrom queried energy users with energy curtailment possibilities areautomatically processed by a computer with a set of instructions forevaluating how to enact each respective curtailment possibility of eachrespective energy user offering a curtailment possibility.
 91. Theenergy management system of claim 90, wherein the computer automaticallyprocessing the responses from queried users totals the respectivecurtailment possibilities from the queried energy users amounts,determines whether the total of respective curtailment possibilities issufficiently large, and, if so, proceeds to schedule a round-robinenergy curtailment rotation pursuant to criteria.
 92. The energymanagement system of claim 90, wherein the computer automaticallyprocessing the responses from queried users totals the respectivecurtailment possibilities from the queried energy users amounts,determines whether the total of respective curtailment possibilities issufficiently large, and, if not, notifies a human user.
 93. The energymanagement system of claim 1, including a preliminary step of functionaltesting for obtaining data and formulating applicable rules, and acontinuous process of learning embedded in a neural net of a modelingagent associated with an energy-using device.
 94. A compilation ofenergy-relevant data, comprising: a stream of energy-related data for atleast one individual energy user within a plurality of energy users. 95.The compilation of claim 94, wherein the at least one individual energyuser is within a multi-building system wherein separate streams of dataare provided for other individual energy users within the multi-buildingsystem.
 96. A data analysis method, comprising leveraging a stream ofenergy-related data for at least one individual energy user within aplurality of energy users, wherein the leveraging includes a comparisonagainst historic data for the device.
 97. The data analysis method ofclaim 96, wherein the leveraging includes computer-based searching forrapid deviation from a historic pattern.
 98. A method of determiningwhether to repair or replace an individual energy user, comprising:reviewing a stream of energy-related data for the individual energyuser, wherein the individual energy user is contained within a pluralityof energy users.
 99. The method of claim 98, wherein the plurality ofenergy users are contained within a multi-building system.
 100. Anenergy management system for automatically achieving energy curtailmentin a multi-building system, comprising: immediate automatic querying ofenergy users within the building system for energy curtailmentpossibilities; automatic receipt of responses from queried energy userswith energy curtailment possibilities; automatic processing of energycurtailment possibilities into a round-robin curtailment rotation. 101.The energy management system of claim 100, wherein the immediateautomatic querying is directly or indirectly activated based on arequest by a local independent system operator, a power authority or autility supplier.
 102. The energy management system of claim 100,wherein the round-robin curtailment rotation is executed and achievesenergy consumption reduction.
 103. The energy management system of claim102, wherein the energy consumption reduction occurs during an energyemergency.
 104. The energy management system of claim 103, wherein theenergy emergency is declared by a local independent system operator, apower authority, a utility supplier, or a governmental authority. 105.The energy management system of claim 100, wherein no human iscontrolling.
 106. The energy management system of claim 100, wherein theround-robin curtailment rotation has been called in order that energymay be sold back into the grid.
 107. The energy management system ofclaim 100, wherein maximum energy curtailment is achieved with minimalimpact to occupants of buildings in the building system.
 108. The energymanagement system of claim 100, wherein maximum energy curtailment isachieved with no greater than a certain defined level of impact tooccupants of buildings in the building system.
 109. The energymanagement system of claim 100, wherein the multi-building system isowned by an owner selected from the group consisting of a commercialentity, a university and a government.
 110. The system of claim 1,wherein each energy user has associated therewith a dedicated neuralnetwork that continuously learns operating characteristics of saidenergy user associated with the dedicated neural network, whereinforward and backward reasoning and forecastability are provided. 111.The system of claim 1, including at least one modeling agent and/or atleast one forecasting agent.
 112. The system of claim 1, wherein thesystem is autonomous, artificial-intelligence based, real-time, over theInternet.